diff --git a/RecoTracker/LST/plugins/alpaka/LSTModulesDevESProducer.cc b/RecoTracker/LST/plugins/alpaka/LSTModulesDevESProducer.cc
index d0e103b1e315b..7152da9ed13c7 100644
--- a/RecoTracker/LST/plugins/alpaka/LSTModulesDevESProducer.cc
+++ b/RecoTracker/LST/plugins/alpaka/LSTModulesDevESProducer.cc
@@ -13,16 +13,23 @@
 namespace ALPAKA_ACCELERATOR_NAMESPACE {
 
   class LSTModulesDevESProducer : public ESProducer {
+  private:
+    std::string ptCutLabel_;
+
   public:
-    LSTModulesDevESProducer(edm::ParameterSet const& iConfig) : ESProducer(iConfig) { setWhatProduced(this); }
+    LSTModulesDevESProducer(edm::ParameterSet const& iConfig)
+        : ESProducer(iConfig), ptCutLabel_(iConfig.getParameter<std::string>("ptCutLabel")) {
+      setWhatProduced(this, ptCutLabel_);
+    }
 
     static void fillDescriptions(edm::ConfigurationDescriptions& descriptions) {
       edm::ParameterSetDescription desc;
+      desc.add<std::string>("ptCutLabel", "0.8");
       descriptions.addWithDefaultLabel(desc);
     }
 
     std::unique_ptr<lst::LSTESData<DevHost>> produce(TrackerRecoGeometryRecord const& iRecord) {
-      return lst::loadAndFillESHost();
+      return lst::loadAndFillESHost(ptCutLabel_);
     }
   };
 
diff --git a/RecoTracker/LST/plugins/alpaka/LSTProducer.cc b/RecoTracker/LST/plugins/alpaka/LSTProducer.cc
index 7eb6c57ade05c..4b83dd3693624 100644
--- a/RecoTracker/LST/plugins/alpaka/LSTProducer.cc
+++ b/RecoTracker/LST/plugins/alpaka/LSTProducer.cc
@@ -28,8 +28,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE {
     LSTProducer(edm::ParameterSet const& config)
         : lstPixelSeedInputToken_{consumes(config.getParameter<edm::InputTag>("pixelSeedInput"))},
           lstPhase2OTHitsInputToken_{consumes(config.getParameter<edm::InputTag>("phase2OTHitsInput"))},
-          lstESToken_{esConsumes()},
+          lstESToken_{esConsumes(edm::ESInputTag("", config.getParameter<std::string>("ptCutLabel")))},
           verbose_(config.getParameter<bool>("verbose")),
+          ptCut_(config.getParameter<double>("ptCut")),
           nopLSDupClean_(config.getParameter<bool>("nopLSDupClean")),
           tcpLSTriplets_(config.getParameter<bool>("tcpLSTriplets")),
           lstOutputToken_{produces()} {}
@@ -43,6 +44,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE {
 
       lst_.run(event.queue(),
                verbose_,
+               static_cast<float>(ptCut_),
                &lstESDeviceData,
                pixelSeeds.px(),
                pixelSeeds.py(),
@@ -78,6 +80,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE {
       desc.add<edm::InputTag>("pixelSeedInput", edm::InputTag{"lstPixelSeedInputProducer"});
       desc.add<edm::InputTag>("phase2OTHitsInput", edm::InputTag{"lstPhase2OTHitsInputProducer"});
       desc.add<bool>("verbose", false);
+      desc.add<double>("ptCut", 0.8);
+      desc.add<std::string>("ptCutLabel", "0.8");
       desc.add<bool>("nopLSDupClean", false);
       desc.add<bool>("tcpLSTriplets", false);
       descriptions.addWithDefaultLabel(desc);
@@ -87,7 +91,10 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE {
     edm::EDGetTokenT<LSTPixelSeedInput> lstPixelSeedInputToken_;
     edm::EDGetTokenT<LSTPhase2OTHitsInput> lstPhase2OTHitsInputToken_;
     device::ESGetToken<lst::LSTESData<Device>, TrackerRecoGeometryRecord> lstESToken_;
-    const bool verbose_, nopLSDupClean_, tcpLSTriplets_;
+    const bool verbose_;
+    const double ptCut_;
+    const bool nopLSDupClean_;
+    const bool tcpLSTriplets_;
     edm::EDPutTokenT<LSTOutput> lstOutputToken_;
 
     lst::LST lst_;
diff --git a/RecoTracker/LSTCore/BuildFile.xml b/RecoTracker/LSTCore/BuildFile.xml
index a58a1898046ae..8dd2b885bf1b4 100644
--- a/RecoTracker/LSTCore/BuildFile.xml
+++ b/RecoTracker/LSTCore/BuildFile.xml
@@ -2,7 +2,6 @@
 <use name="boost_header"/>
 <use name="root"/>
 <use name="HeterogeneousCore/AlpakaInterface"/>
-<flags CXXFLAGS="-DPT_CUT=0.8"/>
 <use name="RecoTracker/LSTCore" for="alpaka"/>
 <flags ALPAKA_BACKENDS="1"/>
 <export>
diff --git a/RecoTracker/LSTCore/interface/Common.h b/RecoTracker/LSTCore/interface/Common.h
index f65ca7a50d867..32337d8f409ae 100644
--- a/RecoTracker/LSTCore/interface/Common.h
+++ b/RecoTracker/LSTCore/interface/Common.h
@@ -20,11 +20,6 @@ namespace lst {
   // Named types for LST objects
   enum LSTObjType { T5 = 4, pT3 = 5, pT5 = 7, pLS = 8 };
 
-// If a compile time flag does not define PT_CUT, default to 0.8 (GeV)
-#ifndef PT_CUT
-  constexpr float PT_CUT = 0.8f;
-#endif
-
   constexpr unsigned int max_blocks = 80;
   constexpr unsigned int max_connected_modules = 40;
 
diff --git a/RecoTracker/LSTCore/interface/LSTESData.h b/RecoTracker/LSTCore/interface/LSTESData.h
index 45887d3cb1fea..bfa10186f8f2e 100644
--- a/RecoTracker/LSTCore/interface/LSTESData.h
+++ b/RecoTracker/LSTCore/interface/LSTESData.h
@@ -40,7 +40,7 @@ namespace lst {
           pixelMapping(pixelMappingIn) {}
   };
 
-  std::unique_ptr<LSTESData<alpaka_common::DevHost>> loadAndFillESHost();
+  std::unique_ptr<LSTESData<alpaka_common::DevHost>> loadAndFillESHost(std::string& ptCutLabel);
 
 }  // namespace lst
 
diff --git a/RecoTracker/LSTCore/interface/QuintupletsSoA.h b/RecoTracker/LSTCore/interface/QuintupletsSoA.h
index 4ece80cd11ddd..e2991a7fe28d3 100644
--- a/RecoTracker/LSTCore/interface/QuintupletsSoA.h
+++ b/RecoTracker/LSTCore/interface/QuintupletsSoA.h
@@ -28,7 +28,9 @@ namespace lst {
                       SOA_COLUMN(float, regressionF),
                       SOA_COLUMN(float, rzChiSquared),  // r-z only chi2
                       SOA_COLUMN(float, chiSquared),
-                      SOA_COLUMN(float, nonAnchorChiSquared));
+                      SOA_COLUMN(float, nonAnchorChiSquared),
+                      SOA_COLUMN(float, dBeta1),
+                      SOA_COLUMN(float, dBeta2));
 
   using QuintupletsSoA = QuintupletsSoALayout<>;
   using Quintuplets = QuintupletsSoA::View;
diff --git a/RecoTracker/LSTCore/interface/alpaka/Common.h b/RecoTracker/LSTCore/interface/alpaka/Common.h
index 7a1feabfcf076..fd59555fe8588 100644
--- a/RecoTracker/LSTCore/interface/alpaka/Common.h
+++ b/RecoTracker/LSTCore/interface/alpaka/Common.h
@@ -49,7 +49,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
   ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float k2Rinv1GeVf = (2.99792458e-3 * 3.8) / 2;
   ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kR1GeVf = 1. / (2.99792458e-3 * 3.8);
   ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kSinAlphaMax = 0.95;
-  ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float ptCut = PT_CUT;
   ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kDeltaZLum = 15.0;
   ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kPixelPSZpitch = 0.15;
   ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kStripPSZpitch = 2.4;
@@ -60,22 +59,27 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
   // To be updated with std::numeric_limits<float>::infinity() in the code and data files
   ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kVerticalModuleSlope = 123456789.0;
 
-  namespace t5dnn {
-
-    // Working points matching LST fake rate (43.9%) or signal acceptance (82.0%)
-    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kLSTWp1 = 0.3418833f;  // 94.0% TPR, 43.9% FPR
-    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kLSTWp2 = 0.6177366f;  // 82.0% TPR, 20.0% FPR
-    // Other working points
-    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp70 = 0.7776195f;    // 70.0% TPR, 10.0% FPR
-    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp75 = 0.7181118f;    // 75.0% TPR, 13.5% FPR
-    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp80 = 0.6492643f;    // 80.0% TPR, 17.9% FPR
-    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp85 = 0.5655319f;    // 85.0% TPR, 23.8% FPR
-    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp90 = 0.4592205f;    // 90.0% TPR, 32.6% FPR
-    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp95 = 0.3073708f;    // 95.0% TPR, 47.7% FPR
-    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp97p5 = 0.2001348f;  // 97.5% TPR, 61.2% FPR
-    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp99 = 0.1120605f;    // 99.0% TPR, 75.9% FPR
-    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp99p9 = 0.0218196f;  // 99.9% TPR, 95.4% FPR
+  ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kMiniDeltaTilted[3] = {0.26f, 0.26f, 0.26f};
+  ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kMiniDeltaFlat[6] = {0.26f, 0.16f, 0.16f, 0.18f, 0.18f, 0.18f};
+  ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kMiniDeltaLooseTilted[3] = {0.4f, 0.4f, 0.4f};
+  ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kMiniDeltaEndcap[5][15] = {
+      {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
+      {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
+      {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.18f, 0.18f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
+      {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.18f, 0.18f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
+      {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.18f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f}};
 
+  namespace t5dnn {
+    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kZ_max = 267.2349854f;
+    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kR_max = 110.1099396f;
+    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kEta_norm = 2.5f;
+    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kPhi_norm = kPi;
+    // pt, eta binned
+    constexpr unsigned int kPtBins = 2;
+    constexpr unsigned int kEtaBins = 10;
+    ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp[kPtBins][kEtaBins] = {
+        {0.4493, 0.4939, 0.5715, 0.6488, 0.5709, 0.5938, 0.7164, 0.7565, 0.8103, 0.8593},
+        {0.4488, 0.4448, 0.5067, 0.5929, 0.4836, 0.4112, 0.4968, 0.4403, 0.5597, 0.5067}};
   }  // namespace t5dnn
 
 }  // namespace ALPAKA_ACCELERATOR_NAMESPACE::lst
diff --git a/RecoTracker/LSTCore/interface/alpaka/LST.h b/RecoTracker/LSTCore/interface/alpaka/LST.h
index 40d912de3f291..5fe369b9cd22b 100644
--- a/RecoTracker/LSTCore/interface/alpaka/LST.h
+++ b/RecoTracker/LSTCore/interface/alpaka/LST.h
@@ -17,6 +17,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
 
     void run(Queue& queue,
              bool verbose,
+             const float ptCut,
              LSTESData<Device> const* deviceESData,
              std::vector<float> const& see_px,
              std::vector<float> const& see_py,
@@ -63,7 +64,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                       std::vector<unsigned int> const& ph2_detId,
                       std::vector<float> const& ph2_x,
                       std::vector<float> const& ph2_y,
-                      std::vector<float> const& ph2_z);
+                      std::vector<float> const& ph2_z,
+                      const float ptCut);
 
     void getOutput(LSTEvent& event);
 
diff --git a/RecoTracker/LSTCore/src/LSTESData.cc b/RecoTracker/LSTCore/src/LSTESData.cc
index 66163d39beb2e..dad8522bbe2cd 100644
--- a/RecoTracker/LSTCore/src/LSTESData.cc
+++ b/RecoTracker/LSTCore/src/LSTESData.cc
@@ -10,25 +10,25 @@
 
 namespace {
   std::string geometryDataDir() {
-    const char* path_lst_base = std::getenv("LST_BASE");
+    std::string path_str, path;
     const char* path_tracklooperdir = std::getenv("TRACKLOOPERDIR");
-    std::string path_str;
-    if (path_lst_base != nullptr) {
-      path_str = path_lst_base;
-    } else if (path_tracklooperdir != nullptr) {
+    std::stringstream search_path;
+    search_path << std::getenv("CMSSW_SEARCH_PATH");
+
+    while (std::getline(search_path, path, ':')) {
+      if (std::filesystem::exists(path + "/RecoTracker/LSTCore/data")) {
+        path_str = path;
+        break;
+      }
+    }
+
+    if (path_str.empty()) {
       path_str = path_tracklooperdir;
-      path_str += "/../";
+      path_str += "/..";
     } else {
-      std::stringstream search_path(std::getenv("CMSSW_SEARCH_PATH"));
-      std::string path;
-      while (std::getline(search_path, path, ':')) {
-        if (std::filesystem::exists(path + "/RecoTracker/LSTCore/data")) {
-          path_str = path;
-          break;
-        }
-      }
       path_str += "/RecoTracker/LSTCore";
     }
+
     return path_str;
   }
 
@@ -43,21 +43,22 @@ namespace {
   void loadMapsHost(lst::MapPLStoLayer& pLStoLayer,
                     lst::EndcapGeometry& endcapGeometry,
                     lst::TiltedGeometry& tiltedGeometry,
-                    lst::ModuleConnectionMap& moduleConnectionMap) {
+                    lst::ModuleConnectionMap& moduleConnectionMap,
+                    std::string& ptCutLabel) {
     // Module orientation information (DrDz or phi angles)
-    auto endcap_geom =
-        get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt0.8/endcap_orientation.bin");
-    auto tilted_geom = get_absolute_path_after_check_file_exists(
-        geometryDataDir() + "/data/OT800_IT615_pt0.8/tilted_barrel_orientation.bin");
+    auto endcap_geom = get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt" +
+                                                                 ptCutLabel + "/endcap_orientation.bin");
+    auto tilted_geom = get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt" +
+                                                                 ptCutLabel + "/tilted_barrel_orientation.bin");
     // Module connection map (for line segment building)
-    auto mappath = get_absolute_path_after_check_file_exists(
-        geometryDataDir() + "/data/OT800_IT615_pt0.8/module_connection_tracing_merged.bin");
+    auto mappath = get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt" + ptCutLabel +
+                                                             "/module_connection_tracing_merged.bin");
 
     endcapGeometry.load(endcap_geom);
     tiltedGeometry.load(tilted_geom);
     moduleConnectionMap.load(mappath);
 
-    auto pLSMapDir = geometryDataDir() + "/data/OT800_IT615_pt0.8/pixelmap/pLS_map";
+    auto pLSMapDir = geometryDataDir() + "/data/OT800_IT615_pt" + ptCutLabel + "/pixelmap/pLS_map";
     const std::array<std::string, 4> connects{
         {"_layer1_subdet5", "_layer2_subdet5", "_layer1_subdet4", "_layer2_subdet4"}};
     std::string path;
@@ -78,7 +79,7 @@ namespace {
   }
 }  // namespace
 
-std::unique_ptr<lst::LSTESData<alpaka_common::DevHost>> lst::loadAndFillESHost() {
+std::unique_ptr<lst::LSTESData<alpaka_common::DevHost>> lst::loadAndFillESHost(std::string& ptCutLabel) {
   uint16_t nModules;
   uint16_t nLowerModules;
   unsigned int nPixels;
@@ -87,7 +88,7 @@ std::unique_ptr<lst::LSTESData<alpaka_common::DevHost>> lst::loadAndFillESHost()
   TiltedGeometry tiltedGeometry;
   PixelMap pixelMapping;
   ModuleConnectionMap moduleConnectionMap;
-  ::loadMapsHost(pLStoLayer, endcapGeometry, tiltedGeometry, moduleConnectionMap);
+  ::loadMapsHost(pLStoLayer, endcapGeometry, tiltedGeometry, moduleConnectionMap, ptCutLabel);
 
   auto endcapGeometryDev =
       std::make_shared<EndcapGeometryDevHostCollection>(endcapGeometry.nEndCapMap, cms::alpakatools::host());
@@ -98,8 +99,8 @@ std::unique_ptr<lst::LSTESData<alpaka_common::DevHost>> lst::loadAndFillESHost()
               endcapGeometry.geoMapPhi_buf.data(),
               endcapGeometry.nEndCapMap * sizeof(float));
 
-  auto path =
-      get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt0.8/sensor_centroids.bin");
+  auto path = get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt" + ptCutLabel +
+                                                        "/sensor_centroids.bin");
   auto modulesBuffers = lst::loadModulesFromFile(pLStoLayer,
                                                  path.c_str(),
                                                  nModules,
diff --git a/RecoTracker/LSTCore/src/alpaka/Kernels.h b/RecoTracker/LSTCore/src/alpaka/Kernels.h
index c642f2427fa84..13d0d2b0e1202 100644
--- a/RecoTracker/LSTCore/src/alpaka/Kernels.h
+++ b/RecoTracker/LSTCore/src/alpaka/Kernels.h
@@ -217,15 +217,22 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
 
           for (unsigned int ix1 = 0; ix1 < nQuintuplets_lowmod1; ix1 += 1) {
             unsigned int ix = quintupletModuleIndices_lowmod1 + ix1;
-            if (quintuplets.partOfPT5()[ix] || (quintuplets.isDup()[ix] & 1))
+            if (quintuplets.isDup()[ix] & 1)
               continue;
 
+            bool isPT5_ix = quintuplets.partOfPT5()[ix];
+
             for (unsigned int jx1 = 0; jx1 < nQuintuplets_lowmod2; jx1++) {
               unsigned int jx = quintupletModuleIndices_lowmod2 + jx1;
               if (ix == jx)
                 continue;
 
-              if (quintuplets.partOfPT5()[jx] || (quintuplets.isDup()[jx] & 1))
+              if (quintuplets.isDup()[jx] & 1)
+                continue;
+
+              bool isPT5_jx = quintuplets.partOfPT5()[jx];
+
+              if (isPT5_ix && isPT5_jx)
                 continue;
 
               float eta1 = __H2F(quintuplets.eta()[ix]);
@@ -249,9 +256,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
               int nMatched = checkHitsT5(ix, jx, quintuplets);
               const int minNHitsForDup_T5 = 5;
               if (dR2 < 0.001f || nMatched >= minNHitsForDup_T5) {
-                if (score_rphisum1 > score_rphisum2) {
+                if (isPT5_jx || score_rphisum1 > score_rphisum2) {
                   rmQuintupletFromMemory(quintuplets, ix, true);
-                } else if (score_rphisum1 < score_rphisum2) {
+                } else if (isPT5_ix || score_rphisum1 < score_rphisum2) {
                   rmQuintupletFromMemory(quintuplets, jx, true);
                 } else {
                   rmQuintupletFromMemory(quintuplets, (ix < jx ? ix : jx), true);
diff --git a/RecoTracker/LSTCore/src/alpaka/LST.cc b/RecoTracker/LSTCore/src/alpaka/LST.cc
index 3c1638677eab2..b2fe87e3a2917 100644
--- a/RecoTracker/LSTCore/src/alpaka/LST.cc
+++ b/RecoTracker/LSTCore/src/alpaka/LST.cc
@@ -75,7 +75,8 @@ void LST::prepareInput(std::vector<float> const& see_px,
                        std::vector<unsigned int> const& ph2_detId,
                        std::vector<float> const& ph2_x,
                        std::vector<float> const& ph2_y,
-                       std::vector<float> const& ph2_z) {
+                       std::vector<float> const& ph2_z,
+                       float const ptCut) {
   in_trkX_.clear();
   in_trkY_.clear();
   in_trkZ_.clear();
@@ -132,7 +133,7 @@ void LST::prepareInput(std::vector<float> const& see_px,
     float eta = p3LH.eta();
     float ptErr = see_ptErr[iSeed];
 
-    if ((ptIn > 0.8 - 2 * ptErr)) {
+    if ((ptIn > ptCut - 2 * ptErr)) {
       XYZVector r3LH(see_stateTrajGlbX[iSeed], see_stateTrajGlbY[iSeed], see_stateTrajGlbZ[iSeed]);
       XYZVector p3PCA(see_px[iSeed], see_py[iSeed], see_pz[iSeed]);
       XYZVector r3PCA(calculateR3FromPCA(p3PCA, see_dxy[iSeed], see_dz[iSeed]));
@@ -149,7 +150,7 @@ void LST::prepareInput(std::vector<float> const& see_px,
 
       if (ptIn >= 2.0)
         pixtype = PixelType::kHighPt;
-      else if (ptIn >= (0.8 - 2 * ptErr) and ptIn < 2.0) {
+      else if (ptIn >= (ptCut - 2 * ptErr) and ptIn < 2.0) {
         if (pixelSegmentDeltaPhiChange >= 0)
           pixtype = PixelType::kLowPtPosCurv;
         else
@@ -255,6 +256,7 @@ void LST::getOutput(LSTEvent& event) {
 
 void LST::run(Queue& queue,
               bool verbose,
+              float const ptCut,
               LSTESData<Device> const* deviceESData,
               std::vector<float> const& see_px,
               std::vector<float> const& see_py,
@@ -277,7 +279,7 @@ void LST::run(Queue& queue,
               std::vector<float> const& ph2_z,
               bool no_pls_dupclean,
               bool tc_pls_triplets) {
-  auto event = LSTEvent(verbose, queue, deviceESData);
+  auto event = LSTEvent(verbose, ptCut, queue, deviceESData);
   prepareInput(see_px,
                see_py,
                see_pz,
@@ -296,7 +298,8 @@ void LST::run(Queue& queue,
                ph2_detId,
                ph2_x,
                ph2_y,
-               ph2_z);
+               ph2_z,
+               ptCut);
 
   event.addHitToEvent(in_trkX_, in_trkY_, in_trkZ_, in_hitId_, in_hitIdxs_);
   event.addPixelSegmentToEvent(in_hitIndices_vec0_,
diff --git a/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc b/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc
index 653b6b0964afa..cb8a2bcbb457e 100644
--- a/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc
+++ b/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc
@@ -202,7 +202,8 @@ void LSTEvent::addPixelSegmentToEvent(std::vector<unsigned int> const& hitIndice
                         createMDArrayRangesGPU_workDiv,
                         CreateMDArrayRangesGPU{},
                         modules_.const_view<ModulesSoA>(),
-                        rangesDC_->view());
+                        rangesDC_->view(),
+                        ptCut_);
 
     auto nTotalMDs_buf_h = cms::alpakatools::make_host_buffer<unsigned int>(queue_);
     auto nTotalMDs_buf_d = cms::alpakatools::make_device_view(queue_, rangesOccupancy.nTotalMDs());
@@ -233,7 +234,8 @@ void LSTEvent::addPixelSegmentToEvent(std::vector<unsigned int> const& hitIndice
                         CreateSegmentArrayRanges{},
                         modules_.const_view<ModulesSoA>(),
                         rangesDC_->view(),
-                        miniDoubletsDC_->const_view<MiniDoubletsSoA>());
+                        miniDoubletsDC_->const_view<MiniDoubletsSoA>(),
+                        ptCut_);
 
     auto rangesOccupancy = rangesDC_->view();
     auto nTotalSegments_view_h = cms::alpakatools::make_host_view(nTotalSegments_);
@@ -346,7 +348,8 @@ void LSTEvent::createMiniDoublets() {
                       createMDArrayRangesGPU_workDiv,
                       CreateMDArrayRangesGPU{},
                       modules_.const_view<ModulesSoA>(),
-                      rangesDC_->view());
+                      rangesDC_->view(),
+                      ptCut_);
 
   auto nTotalMDs_buf_h = cms::alpakatools::make_host_buffer<unsigned int>(queue_);
   auto nTotalMDs_buf_d = cms::alpakatools::make_device_view(queue_, rangesOccupancy.nTotalMDs());
@@ -381,7 +384,8 @@ void LSTEvent::createMiniDoublets() {
                       hitsDC_->const_view<HitsRangesSoA>(),
                       miniDoubletsDC_->view<MiniDoubletsSoA>(),
                       miniDoubletsDC_->view<MiniDoubletsOccupancySoA>(),
-                      rangesDC_->const_view());
+                      rangesDC_->const_view(),
+                      ptCut_);
 
   WorkDiv1D const addMiniDoubletRangesToEventExplicit_workDiv = createWorkDiv<Vec1D>({1}, {1024}, {1});
 
@@ -427,7 +431,8 @@ void LSTEvent::createSegmentsWithModuleMap() {
                       miniDoubletsDC_->const_view<MiniDoubletsOccupancySoA>(),
                       segmentsDC_->view<SegmentsSoA>(),
                       segmentsDC_->view<SegmentsOccupancySoA>(),
-                      rangesDC_->const_view());
+                      rangesDC_->const_view(),
+                      ptCut_);
 
   WorkDiv1D const addSegmentRangesToEventExplicit_workDiv = createWorkDiv<Vec1D>({1}, {1024}, {1});
 
@@ -452,7 +457,8 @@ void LSTEvent::createTriplets() {
                         CreateTripletArrayRanges{},
                         modules_.const_view<ModulesSoA>(),
                         rangesDC_->view(),
-                        segmentsDC_->const_view<SegmentsOccupancySoA>());
+                        segmentsDC_->const_view<SegmentsOccupancySoA>(),
+                        ptCut_);
 
     // TODO: Why are we pulling this back down only to put it back on the device in a new struct?
     auto rangesOccupancy = rangesDC_->view();
@@ -537,7 +543,8 @@ void LSTEvent::createTriplets() {
                       tripletsDC_->view<TripletsOccupancySoA>(),
                       rangesDC_->const_view(),
                       index_gpu_buf.data(),
-                      nonZeroModules);
+                      nonZeroModules,
+                      ptCut_);
 
   WorkDiv1D const addTripletRangesToEventExplicit_workDiv = createWorkDiv<Vec1D>({1}, {1024}, {1});
 
@@ -822,7 +829,8 @@ void LSTEvent::createPixelTriplets() {
                       pixelTripletsDC_->view(),
                       connectedPixelSize_dev_buf.data(),
                       connectedPixelIndex_dev_buf.data(),
-                      nInnerSegments);
+                      nInnerSegments,
+                      ptCut_);
 
 #ifdef WARNINGS
   auto nPixelTriplets_buf = cms::alpakatools::make_host_buffer<unsigned int>(queue_);
@@ -853,7 +861,8 @@ void LSTEvent::createQuintuplets() {
                       CreateEligibleModulesListForQuintuplets{},
                       modules_.const_view<ModulesSoA>(),
                       tripletsDC_->const_view<TripletsOccupancySoA>(),
-                      rangesDC_->view());
+                      rangesDC_->view(),
+                      ptCut_);
 
   auto nEligibleT5Modules_buf = cms::alpakatools::make_host_buffer<uint16_t>(queue_);
   auto nTotalQuintuplets_buf = cms::alpakatools::make_host_buffer<unsigned int>(queue_);
@@ -904,7 +913,8 @@ void LSTEvent::createQuintuplets() {
                       quintupletsDC_->view<QuintupletsSoA>(),
                       quintupletsDC_->view<QuintupletsOccupancySoA>(),
                       rangesDC_->const_view(),
-                      nEligibleT5Modules);
+                      nEligibleT5Modules,
+                      ptCut_);
 
   Vec3D const threadsPerBlockDupQuint{1, 16, 16};
   Vec3D const blocksPerGridDupQuint{max_blocks, 1, 1};
@@ -1065,7 +1075,8 @@ void LSTEvent::createPixelQuintuplets() {
                       connectedPixelSize_dev_buf.data(),
                       connectedPixelIndex_dev_buf.data(),
                       nInnerSegments,
-                      rangesDC_->const_view());
+                      rangesDC_->const_view(),
+                      ptCut_);
 
   Vec3D const threadsPerBlockDupPix{1, 16, 16};
   Vec3D const blocksPerGridDupPix{1, max_blocks, 1};
@@ -1191,8 +1202,9 @@ void LSTEvent::addQuintupletsToEventExplicit() {
 
   // FIXME: replace by ES host data
   auto module_subdets_buf = cms::alpakatools::make_host_buffer<short[]>(queue_, nLowerModules_);
-  auto module_subdets_view = cms::alpakatools::make_device_view(queue_, modules.subdets(), modules.metadata().size());
-  alpaka::memcpy(queue_, module_subdets_buf, module_subdets_view, nModules_);
+  auto module_subdets_view =
+      cms::alpakatools::make_device_view(queue_, modules.subdets(), nLowerModules_);  // only lower modules
+  alpaka::memcpy(queue_, module_subdets_buf, module_subdets_view, nLowerModules_);
 
   auto module_layers_buf = cms::alpakatools::make_host_buffer<short[]>(queue_, nLowerModules_);
   auto module_layers_view =
diff --git a/RecoTracker/LSTCore/src/alpaka/LSTEvent.h b/RecoTracker/LSTCore/src/alpaka/LSTEvent.h
index d4c659a199515..a883436a11266 100644
--- a/RecoTracker/LSTCore/src/alpaka/LSTEvent.h
+++ b/RecoTracker/LSTCore/src/alpaka/LSTEvent.h
@@ -36,6 +36,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
   class LSTEvent {
   private:
     Queue& queue_;
+    const float ptCut_;
 
     std::array<unsigned int, 6> n_minidoublets_by_layer_barrel_{};
     std::array<unsigned int, 5> n_minidoublets_by_layer_endcap_{};
@@ -81,8 +82,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
 
   public:
     // Constructor used for CMSSW integration. Uses an external queue.
-    LSTEvent(bool verbose, Queue& q, const LSTESData<Device>* deviceESData)
+    LSTEvent(bool verbose, const float pt_cut, Queue& q, const LSTESData<Device>* deviceESData)
         : queue_(q),
+          ptCut_(pt_cut),
           nModules_(deviceESData->nModules),
           nLowerModules_(deviceESData->nLowerModules),
           nPixels_(deviceESData->nPixels),
@@ -90,7 +92,12 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
           modules_(*deviceESData->modules),
           pixelMapping_(*deviceESData->pixelMapping),
           endcapGeometry_(*deviceESData->endcapGeometry),
-          addObjects_(verbose) {}
+          addObjects_(verbose) {
+      if (pt_cut < 0.6f) {
+        throw std::invalid_argument("Minimum pT cut must be at least 0.6 GeV. Provided value: " +
+                                    std::to_string(pt_cut));
+      }
+    }
     void initSync();        // synchronizes, for standalone usage
     void resetEventSync();  // synchronizes, for standalone usage
     void wait() const { alpaka::wait(queue_); }
diff --git a/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h b/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h
index c2af864f84d12..4255d651dbce2 100644
--- a/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h
+++ b/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h
@@ -106,35 +106,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
   }
 
   ALPAKA_FN_ACC ALPAKA_FN_INLINE float moduleGapSize(ModulesConst modules, uint16_t moduleIndex) {
-    float miniDeltaTilted[3] = {0.26f, 0.26f, 0.26f};
-    float miniDeltaFlat[6] = {0.26f, 0.16f, 0.16f, 0.18f, 0.18f, 0.18f};
-    float miniDeltaLooseTilted[3] = {0.4f, 0.4f, 0.4f};
-    float miniDeltaEndcap[5][15];
-
-    for (size_t i = 0; i < 5; i++) {
-      for (size_t j = 0; j < 15; j++) {
-        if (i == 0 || i == 1) {
-          if (j < 10) {
-            miniDeltaEndcap[i][j] = 0.4f;
-          } else {
-            miniDeltaEndcap[i][j] = 0.18f;
-          }
-        } else if (i == 2 || i == 3) {
-          if (j < 8) {
-            miniDeltaEndcap[i][j] = 0.4f;
-          } else {
-            miniDeltaEndcap[i][j] = 0.18f;
-          }
-        } else {
-          if (j < 9) {
-            miniDeltaEndcap[i][j] = 0.4f;
-          } else {
-            miniDeltaEndcap[i][j] = 0.18f;
-          }
-        }
-      }
-    }
-
     unsigned int iL = modules.layers()[moduleIndex] - 1;
     unsigned int iR = modules.rings()[moduleIndex] - 1;
     short subdet = modules.subdets()[moduleIndex];
@@ -143,22 +114,27 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     float moduleSeparation = 0;
 
     if (subdet == Barrel and side == Center) {
-      moduleSeparation = miniDeltaFlat[iL];
+      moduleSeparation = kMiniDeltaFlat[iL];
     } else if (isTighterTiltedModules(modules, moduleIndex)) {
-      moduleSeparation = miniDeltaTilted[iL];
+      moduleSeparation = kMiniDeltaTilted[iL];
     } else if (subdet == Endcap) {
-      moduleSeparation = miniDeltaEndcap[iL][iR];
+      moduleSeparation = kMiniDeltaEndcap[iL][iR];
     } else  //Loose tilted modules
     {
-      moduleSeparation = miniDeltaLooseTilted[iL];
+      moduleSeparation = kMiniDeltaLooseTilted[iL];
     }
 
     return moduleSeparation;
   }
 
   template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE float dPhiThreshold(
-      TAcc const& acc, float rt, ModulesConst modules, uint16_t moduleIndex, float dPhi = 0, float dz = 0) {
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE float dPhiThreshold(TAcc const& acc,
+                                                     float rt,
+                                                     ModulesConst modules,
+                                                     uint16_t moduleIndex,
+                                                     const float ptCut,
+                                                     float dPhi = 0,
+                                                     float dz = 0) {
     // =================================================================
     // Various constants
     // =================================================================
@@ -403,7 +379,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                      float xUpper,
                                                      float yUpper,
                                                      float zUpper,
-                                                     float rtUpper) {
+                                                     float rtUpper,
+                                                     const float ptCut) {
     dz = zLower - zUpper;
     const float dzCut = modules.moduleType()[lowerModuleIndex] == PS ? 2.f : 10.f;
     const float sign = ((dz > 0) - (dz < 0)) * ((zLower > 0) - (zLower < 0));
@@ -415,8 +392,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     float miniCut = 0;
 
     miniCut = modules.moduleLayerType()[lowerModuleIndex] == Pixel
-                  ? dPhiThreshold(acc, rtLower, modules, lowerModuleIndex)
-                  : dPhiThreshold(acc, rtUpper, modules, lowerModuleIndex);
+                  ? dPhiThreshold(acc, rtLower, modules, lowerModuleIndex, ptCut)
+                  : dPhiThreshold(acc, rtUpper, modules, lowerModuleIndex, ptCut);
 
     // Cut #2: dphi difference
     // Ref to original code: https://github.com/slava77/cms-tkph2-ntuple/blob/184d2325147e6930030d3d1f780136bc2dd29ce6/doubletAnalysis.C#L3085
@@ -530,7 +507,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                      float xUpper,
                                                      float yUpper,
                                                      float zUpper,
-                                                     float rtUpper) {
+                                                     float rtUpper,
+                                                     const float ptCut) {
     // There are series of cuts that applies to mini-doublet in a "endcap" region
     // Cut #1 : dz cut. The dz difference can't be larger than 1cm. (max separation is 4mm for modules in the endcap)
     // Ref to original code: https://github.com/slava77/cms-tkph2-ntuple/blob/184d2325147e6930030d3d1f780136bc2dd29ce6/doubletAnalysis.C#L3093
@@ -603,8 +581,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
 
     float miniCut = 0;
     miniCut = modules.moduleLayerType()[lowerModuleIndex] == Pixel
-                  ? dPhiThreshold(acc, rtLower, modules, lowerModuleIndex, dPhi, dz)
-                  : dPhiThreshold(acc, rtUpper, modules, lowerModuleIndex, dPhi, dz);
+                  ? dPhiThreshold(acc, rtLower, modules, lowerModuleIndex, ptCut, dPhi, dz)
+                  : dPhiThreshold(acc, rtUpper, modules, lowerModuleIndex, ptCut, dPhi, dz);
 
     if (alpaka::math::abs(acc, dPhi) >= miniCut)
       return false;
@@ -641,7 +619,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                float xUpper,
                                                float yUpper,
                                                float zUpper,
-                                               float rtUpper) {
+                                               float rtUpper,
+                                               const float ptCut) {
     if (modules.subdets()[lowerModuleIndex] == Barrel) {
       return runMiniDoubletDefaultAlgoBarrel(acc,
                                              modules,
@@ -664,7 +643,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                              xUpper,
                                              yUpper,
                                              zUpper,
-                                             rtUpper);
+                                             rtUpper,
+                                             ptCut);
     } else {
       return runMiniDoubletDefaultAlgoEndcap(acc,
                                              modules,
@@ -687,7 +667,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                              xUpper,
                                              yUpper,
                                              zUpper,
-                                             rtUpper);
+                                             rtUpper,
+                                             ptCut);
     }
   }
 
@@ -699,7 +680,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                   HitsRangesConst hitsRanges,
                                   MiniDoublets mds,
                                   MiniDoubletsOccupancy mdsOccupancy,
-                                  ObjectRangesConst ranges) const {
+                                  ObjectRangesConst ranges,
+                                  const float ptCut) const {
       auto const globalThreadIdx = alpaka::getIdx<alpaka::Grid, alpaka::Threads>(acc);
       auto const gridThreadExtent = alpaka::getWorkDiv<alpaka::Grid, alpaka::Threads>(acc);
 
@@ -754,13 +736,15 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                    xUpper,
                                                    yUpper,
                                                    zUpper,
-                                                   rtUpper);
+                                                   rtUpper,
+                                                   ptCut);
           if (success) {
             int totOccupancyMDs = alpaka::atomicAdd(
                 acc, &mdsOccupancy.totOccupancyMDs()[lowerModuleIndex], 1u, alpaka::hierarchy::Threads{});
             if (totOccupancyMDs >= (ranges.miniDoubletModuleOccupancy()[lowerModuleIndex])) {
 #ifdef WARNINGS
-              printf("Mini-doublet excess alert! Module index =  %d\n", lowerModuleIndex);
+              printf(
+                  "Mini-doublet excess alert! Module index = %d, Occupancy = %d\n", lowerModuleIndex, totOccupancyMDs);
 #endif
             } else {
               int mdModuleIndex =
@@ -790,9 +774,38 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     }
   };
 
+  // Helper function to determine eta bin for occupancies
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE int getEtaBin(const float module_eta) {
+    if (module_eta < 0.75f)
+      return 0;
+    else if (module_eta < 1.5f)
+      return 1;
+    else if (module_eta < 2.25f)
+      return 2;
+    else if (module_eta < 3.0f)
+      return 3;
+    return -1;
+  }
+
+  // Helper function to determine category number for occupancies
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE int getCategoryNumber(const short module_layers,
+                                                       const short module_subdets,
+                                                       const short module_rings) {
+    if (module_subdets == Barrel) {
+      return (module_layers <= 3) ? 0 : 1;
+    } else if (module_subdets == Endcap) {
+      if (module_layers <= 2) {
+        return (module_rings >= 11) ? 2 : 3;
+      } else {
+        return (module_rings >= 8) ? 2 : 3;
+      }
+    }
+    return -1;
+  }
+
   struct CreateMDArrayRangesGPU {
     template <typename TAcc>
-    ALPAKA_FN_ACC void operator()(TAcc const& acc, ModulesConst modules, ObjectRanges ranges) const {
+    ALPAKA_FN_ACC void operator()(TAcc const& acc, ModulesConst modules, ObjectRanges ranges, const float ptCut) const {
       // implementation is 1D with a single block
       static_assert(std::is_same_v<TAcc, ALPAKA_ACCELERATOR_NAMESPACE::Acc1D>, "Should be Acc1D");
       ALPAKA_ASSERT_ACC((alpaka::getWorkDiv<alpaka::Grid, alpaka::Blocks>(acc)[0] == 1));
@@ -807,67 +820,43 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
       }
       alpaka::syncBlockThreads(acc);
 
+      // Occupancy matrix for 0.8 GeV pT Cut
+      constexpr int p08_occupancy_matrix[4][4] = {
+          {49, 42, 37, 41},  // category 0
+          {100, 100, 0, 0},  // category 1
+          {0, 16, 19, 0},    // category 2
+          {0, 14, 20, 25}    // category 3
+      };
+
+      // Occupancy matrix for 0.6 GeV pT Cut, 99.99%
+      constexpr int p06_occupancy_matrix[4][4] = {
+          {60, 57, 54, 48},  // category 0
+          {259, 195, 0, 0},  // category 1
+          {0, 23, 28, 0},    // category 2
+          {0, 25, 25, 33}    // category 3
+      };
+
+      // Select the appropriate occupancy matrix based on ptCut
+      const auto& occupancy_matrix = (ptCut < 0.8f) ? p06_occupancy_matrix : p08_occupancy_matrix;
+
       for (uint16_t i = globalThreadIdx[0]; i < modules.nLowerModules(); i += gridThreadExtent[0]) {
         short module_rings = modules.rings()[i];
         short module_layers = modules.layers()[i];
         short module_subdets = modules.subdets()[i];
         float module_eta = alpaka::math::abs(acc, modules.eta()[i]);
 
-        int category_number;
-        if (module_layers <= 3 && module_subdets == 5)
-          category_number = 0;
-        else if (module_layers >= 4 && module_subdets == 5)
-          category_number = 1;
-        else if (module_layers <= 2 && module_subdets == 4 && module_rings >= 11)
-          category_number = 2;
-        else if (module_layers >= 3 && module_subdets == 4 && module_rings >= 8)
-          category_number = 2;
-        else if (module_layers <= 2 && module_subdets == 4 && module_rings <= 10)
-          category_number = 3;
-        else if (module_layers >= 3 && module_subdets == 4 && module_rings <= 7)
-          category_number = 3;
-        else
-          category_number = -1;
-
-        int eta_number;
-        if (module_eta < 0.75f)
-          eta_number = 0;
-        else if (module_eta < 1.5f)
-          eta_number = 1;
-        else if (module_eta < 2.25f)
-          eta_number = 2;
-        else if (module_eta < 3.0f)
-          eta_number = 3;
-        else
-          eta_number = -1;
-
-        int occupancy;
-        if (category_number == 0 && eta_number == 0)
-          occupancy = 49;
-        else if (category_number == 0 && eta_number == 1)
-          occupancy = 42;
-        else if (category_number == 0 && eta_number == 2)
-          occupancy = 37;
-        else if (category_number == 0 && eta_number == 3)
-          occupancy = 41;
-        else if (category_number == 1)
-          occupancy = 100;
-        else if (category_number == 2 && eta_number == 1)
-          occupancy = 16;
-        else if (category_number == 2 && eta_number == 2)
-          occupancy = 19;
-        else if (category_number == 3 && eta_number == 1)
-          occupancy = 14;
-        else if (category_number == 3 && eta_number == 2)
-          occupancy = 20;
-        else if (category_number == 3 && eta_number == 3)
-          occupancy = 25;
-        else {
-          occupancy = 0;
+        int category_number = getCategoryNumber(module_layers, module_subdets, module_rings);
+        int eta_number = getEtaBin(module_eta);
+
+        int occupancy = 0;
+        if (category_number != -1 && eta_number != -1) {
+          occupancy = occupancy_matrix[category_number][eta_number];
+        }
 #ifdef WARNINGS
+        else {
           printf("Unhandled case in createMDArrayRangesGPU! Module index = %i\n", i);
-#endif
         }
+#endif
 
         unsigned int nTotMDs = alpaka::atomicAdd(acc, &nTotalMDs, occupancy, alpaka::hierarchy::Threads{});
 
diff --git a/RecoTracker/LSTCore/src/alpaka/NeuralNetwork.h b/RecoTracker/LSTCore/src/alpaka/NeuralNetwork.h
index 42605c80e9434..cc1bffa3d928b 100644
--- a/RecoTracker/LSTCore/src/alpaka/NeuralNetwork.h
+++ b/RecoTracker/LSTCore/src/alpaka/NeuralNetwork.h
@@ -1,165 +1,151 @@
 #ifndef RecoTracker_LSTCore_src_alpaka_NeuralNetwork_h
 #define RecoTracker_LSTCore_src_alpaka_NeuralNetwork_h
 
+#include "FWCore/Utilities/interface/CMSUnrollLoop.h"
+
 #include "RecoTracker/LSTCore/interface/alpaka/Common.h"
-#include "RecoTracker/LSTCore/interface/ModulesSoA.h"
-#include "RecoTracker/LSTCore/interface/HitsSoA.h"
 #include "RecoTracker/LSTCore/interface/MiniDoubletsSoA.h"
-#include "RecoTracker/LSTCore/interface/SegmentsSoA.h"
-#include "RecoTracker/LSTCore/interface/TripletsSoA.h"
 
 #include "NeuralNetworkWeights.h"
 
-namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
-
-  namespace t5dnn {
-
-    template <typename TAcc>
-    ALPAKA_FN_ACC ALPAKA_FN_INLINE float runInference(TAcc const& acc,
-                                                      ModulesConst modules,
-                                                      MiniDoubletsConst mds,
-                                                      SegmentsConst segments,
-                                                      TripletsConst triplets,
-                                                      const float* xVec,
-                                                      const float* yVec,
-                                                      const unsigned int* mdIndices,
-                                                      const uint16_t* lowerModuleIndices,
-                                                      unsigned int innerTripletIndex,
-                                                      unsigned int outerTripletIndex,
-                                                      float innerRadius,
-                                                      float outerRadius,
-                                                      float bridgeRadius) {
-      // Unpack x-coordinates of hits
-      float x1 = xVec[0];
-      float x2 = xVec[1];
-      float x3 = xVec[2];
-      float x4 = xVec[3];
-      float x5 = xVec[4];
-      // Unpack y-coordinates of hits
-      float y1 = yVec[0];
-      float y2 = yVec[1];
-      float y3 = yVec[2];
-      float y4 = yVec[3];
-      float y5 = yVec[4];
-      // Unpack module indices
-      unsigned int mdIndex1 = mdIndices[0];
-      unsigned int mdIndex2 = mdIndices[1];
-      unsigned int mdIndex3 = mdIndices[2];
-      unsigned int mdIndex4 = mdIndices[3];
-      unsigned int mdIndex5 = mdIndices[4];
-      // Unpack module indices
-      uint16_t lowerModuleIndex1 = lowerModuleIndices[0];
-      uint16_t lowerModuleIndex2 = lowerModuleIndices[1];
-      uint16_t lowerModuleIndex3 = lowerModuleIndices[2];
-      uint16_t lowerModuleIndex4 = lowerModuleIndices[3];
-      uint16_t lowerModuleIndex5 = lowerModuleIndices[4];
-      // Compute some convenience variables
-      short layer2_adjustment = 0;
-      if (modules.layers()[lowerModuleIndex1] == 1) {
-        layer2_adjustment = 1;  // get upper segment to be in second layer
-      }
-      unsigned int md_idx_for_t5_eta_phi =
-          segments.mdIndices()[triplets.segmentIndices()[innerTripletIndex][0]][layer2_adjustment];
-      bool is_endcap1 = (modules.subdets()[lowerModuleIndex1] == 4);  // true if anchor hit 1 is in the endcap
-      bool is_endcap2 = (modules.subdets()[lowerModuleIndex2] == 4);  // true if anchor hit 2 is in the endcap
-      bool is_endcap3 = (modules.subdets()[lowerModuleIndex3] == 4);  // true if anchor hit 3 is in the endcap
-      bool is_endcap4 = (modules.subdets()[lowerModuleIndex4] == 4);  // true if anchor hit 4 is in the endcap
-      bool is_endcap5 = (modules.subdets()[lowerModuleIndex5] == 4);  // true if anchor hit 5 is in the endcap
-
-      // Build DNN input vector (corresponding output N-tuple branch noted in parenthetical in comment)
-      float x[38] = {
-          alpaka::math::log10(acc, 2 * k2Rinv1GeVf * innerRadius),      // inner T3 pT (t3_pt)
-          mds.anchorEta()[mdIndex1],                                    // inner T3 anchor hit 1 eta (t3_0_eta)
-          mds.anchorPhi()[mdIndex1],                                    // inner T3 anchor hit 1 phi (t3_0_phi)
-          mds.anchorZ()[mdIndex1],                                      // inner T3 anchor hit 1 z (t3_0_z)
-          alpaka::math::sqrt(acc, x1 * x1 + y1 * y1),                   // inner T3 anchor hit 1 r (t3_0_r)
-          float(modules.layers()[lowerModuleIndex1] + 6 * is_endcap1),  // inner T3 anchor hit 1 layer (t3_0_layer)
-          mds.anchorEta()[mdIndex2],                                    // inner T3 anchor hit 2 eta (t3_2_eta)
-          mds.anchorPhi()[mdIndex2],                                    // inner T3 anchor hit 2 phi (t3_2_phi)
-          mds.anchorZ()[mdIndex2],                                      // inner T3 anchor hit 2 z (t3_2_z)
-          alpaka::math::sqrt(acc, x2 * x2 + y2 * y2),                   // inner T3 anchor hit 2 r (t3_2_r)
-          float(modules.layers()[lowerModuleIndex2] + 6 * is_endcap2),  // inner T3 anchor hit 2 layer (t3_2_layer)
-          mds.anchorEta()[mdIndex3],                                    // inner T3 anchor hit 3 eta (t3_4_eta)
-          mds.anchorPhi()[mdIndex3],                                    // inner T3 anchor hit 3 phi (t3_4_phi)
-          mds.anchorZ()[mdIndex3],                                      // inner T3 anchor hit 3 z (t3_4_z)
-          alpaka::math::sqrt(acc, x3 * x3 + y3 * y3),                   // inner T3 anchor hit 3 r (t3_4_r)
-          float(modules.layers()[lowerModuleIndex3] + 6 * is_endcap3),  // inner T3 anchor hit 3 layer (t3_4_layer)
-          alpaka::math::log10(acc, 2 * k2Rinv1GeVf * outerRadius),      // outer T3 pT (t3_pt)
-          mds.anchorEta()[mdIndex3],                                    // outer T3 anchor hit 4 eta (t3_0_eta)
-          mds.anchorPhi()[mdIndex3],                                    // outer T3 anchor hit 4 phi (t3_0_phi)
-          mds.anchorZ()[mdIndex3],                                      // outer T3 anchor hit 3 eta (t3_0_z)
-          alpaka::math::sqrt(acc, x3 * x3 + y3 * y3),                   // outer T3 anchor hit 3 r (t3_0_r)
-          float(modules.layers()[lowerModuleIndex3] + 6 * is_endcap3),  // outer T3 anchor hit 3 layer (t3_0_layer)
-          mds.anchorEta()[mdIndex4],                                    // outer T3 anchor hit 4 eta (t3_2_eta)
-          mds.anchorPhi()[mdIndex4],                                    // outer T3 anchor hit 4 phi (t3_2_phi)
-          mds.anchorZ()[mdIndex4],                                      // outer T3 anchor hit 4 z (t3_2_z)
-          alpaka::math::sqrt(acc, x4 * x4 + y4 * y4),                   // outer T3 anchor hit 4 r (t3_2_r)
-          float(modules.layers()[lowerModuleIndex4] + 6 * is_endcap4),  // outer T3 anchor hit 4 layer (t3_2_layer)
-          mds.anchorEta()[mdIndex5],                                    // outer T3 anchor hit 5 eta (t3_4_eta)
-          mds.anchorPhi()[mdIndex5],                                    // outer T3 anchor hit 5 phi (t3_4_phi)
-          mds.anchorZ()[mdIndex5],                                      // outer T3 anchor hit 5 z (t3_4_z)
-          alpaka::math::sqrt(acc, x5 * x5 + y5 * y5),                   // outer T3 anchor hit 5 r (t3_4_r)
-          float(modules.layers()[lowerModuleIndex5] + 6 * is_endcap5),  // outer T3 anchor hit 5 layer (t3_4_layer)
-          alpaka::math::log10(acc, (innerRadius + outerRadius) * k2Rinv1GeVf),  // T5 pT (t5_pt)
-          mds.anchorEta()[md_idx_for_t5_eta_phi],                               // T5 eta (t5_eta)
-          mds.anchorPhi()[md_idx_for_t5_eta_phi],                               // T5 phi (t5_phi)
-          alpaka::math::log10(acc, innerRadius),                                // T5 inner radius (t5_innerRadius)
-          alpaka::math::log10(acc, bridgeRadius),                               // T5 bridge radius (t5_bridgeRadius)
-          alpaka::math::log10(acc, outerRadius)                                 // T5 outer radius (t5_outerRadius)
-      };
-
-      // (0): Linear(in_features=38, out_features=32, bias=True) => x = x*W_T + b
-      float x_0[32];
-      for (unsigned int col = 0; col < 32; ++col) {
-        x_0[col] = 0;
-        for (unsigned int inner = 0; inner < 38; ++inner) {
-          x_0[col] += x[inner] * wgtT_0[inner][col];
-        }
-        x_0[col] += bias_0[col];
-      }
+namespace ALPAKA_ACCELERATOR_NAMESPACE::lst::t5dnn {
 
-      // (1): ReLU()
-      float x_1[32];
-      for (unsigned int col = 0; col < 32; ++col) {
-        x_1[col] = (x_0[col] > 0.f) ? x_0[col] : 0.f;
-      }
+  template <int FEATURES>
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE void relu_activation(float (&input)[FEATURES]) {
+    CMS_UNROLL_LOOP
+    for (unsigned int col = 0; col < FEATURES; ++col) {
+      input[col] = (input[col] > 0.f) ? input[col] : 0.f;
+    }
+  }
 
-      // (2): Linear(in_features=32, out_features=32, bias=True) => x = x*W_T + b
-      float x_2[32];
-      for (unsigned int col = 0; col < 32; ++col) {
-        x_2[col] = 0;
-        for (unsigned int inner = 0; inner < 32; ++inner) {
-          x_2[col] += x_1[inner] * wgtT_2[inner][col];
-        }
-        x_2[col] += bias_2[col];
-      }
+  template <typename TAcc>
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE float sigmoid_activation(TAcc const& acc, const float x) {
+    return alpaka::math::exp(acc, x) / (alpaka::math::exp(acc, x) + 1.f);
+  }
 
-      // (3): ReLU()
-      float x_3[32];
-      for (unsigned int col = 0; col < 32; ++col) {
-        x_3[col] = (x_2[col] > 0.f) ? x_2[col] : 0.f;
+  template <int IN_FEATURES, int OUT_FEATURES>
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE void linear_layer(const float (&input)[IN_FEATURES],
+                                                   float (&output)[OUT_FEATURES],
+                                                   const float (&weights)[IN_FEATURES][OUT_FEATURES],
+                                                   const float (&biases)[OUT_FEATURES]) {
+    CMS_UNROLL_LOOP
+    for (unsigned int i = 0; i < OUT_FEATURES; ++i) {
+      output[i] = biases[i];
+      CMS_UNROLL_LOOP
+      for (int j = 0; j < IN_FEATURES; ++j) {
+        output[i] += input[j] * weights[j][i];
       }
+    }
+  }
 
-      // (4): Linear(in_features=32, out_features=1, bias=True) => x = x*W_T + b
-      float x_4[1];
-      for (unsigned int col = 0; col < 1; ++col) {
-        x_4[col] = 0;
-        for (unsigned int inner = 0; inner < 32; ++inner) {
-          x_4[col] += x_3[inner] * wgtT_4[inner][col];
-        }
-        x_4[col] += bias_4[col];
-      }
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE float delta_phi(const float phi1, const float phi2) {
+    float delta = phi1 - phi2;
+    // Adjust delta to be within the range [-M_PI, M_PI]
+    if (delta > kPi) {
+      delta -= 2 * kPi;
+    } else if (delta < -kPi) {
+      delta += 2 * kPi;
+    }
 
-      // (5): Sigmoid()
-      float x_5[1];
-      for (unsigned int col = 0; col < 1; ++col) {
-        x_5[col] = alpaka::math::exp(acc, x_4[col]) / (alpaka::math::exp(acc, x_4[col]) + 1);
-      }
+    return delta;
+  }
 
-      return x_5[0];
-    }
+  template <typename TAcc>
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runInference(TAcc const& acc,
+                                                   MiniDoubletsConst mds,
+                                                   const unsigned int mdIndex1,
+                                                   const unsigned int mdIndex2,
+                                                   const unsigned int mdIndex3,
+                                                   const unsigned int mdIndex4,
+                                                   const unsigned int mdIndex5,
+                                                   const float innerRadius,
+                                                   const float outerRadius,
+                                                   const float bridgeRadius) {
+    // Constants
+    constexpr unsigned int kinputFeatures = 23;
+    constexpr unsigned int khiddenFeatures = 32;
+
+    float eta1 = alpaka::math::abs(acc, mds.anchorEta()[mdIndex1]);  // inner T3 anchor hit 1 eta (t3_0_eta)
+    float eta2 = alpaka::math::abs(acc, mds.anchorEta()[mdIndex2]);  // inner T3 anchor hit 2 eta (t3_2_eta)
+    float eta3 = alpaka::math::abs(acc, mds.anchorEta()[mdIndex3]);  // inner T3 anchor hit 3 eta (t3_4_eta)
+    float eta4 = alpaka::math::abs(acc, mds.anchorEta()[mdIndex4]);  // outer T3 anchor hit 4 eta (t3_2_eta)
+    float eta5 = alpaka::math::abs(acc, mds.anchorEta()[mdIndex5]);  // outer T3 anchor hit 5 eta (t3_4_eta)
+
+    float phi1 = mds.anchorPhi()[mdIndex1];  // inner T3 anchor hit 1 phi (t3_0_phi)
+    float phi2 = mds.anchorPhi()[mdIndex2];  // inner T3 anchor hit 2 phi (t3_2_phi)
+    float phi3 = mds.anchorPhi()[mdIndex3];  // inner T3 anchor hit 3 phi (t3_4_phi)
+    float phi4 = mds.anchorPhi()[mdIndex4];  // outer T3 anchor hit 4 phi (t3_2_phi)
+    float phi5 = mds.anchorPhi()[mdIndex5];  // outer T3 anchor hit 5 phi (t3_4_phi)
+
+    float z1 = alpaka::math::abs(acc, mds.anchorZ()[mdIndex1]);  // inner T3 anchor hit 1 z (t3_0_z)
+    float z2 = alpaka::math::abs(acc, mds.anchorZ()[mdIndex2]);  // inner T3 anchor hit 2 z (t3_2_z)
+    float z3 = alpaka::math::abs(acc, mds.anchorZ()[mdIndex3]);  // inner T3 anchor hit 3 z (t3_4_z)
+    float z4 = alpaka::math::abs(acc, mds.anchorZ()[mdIndex4]);  // outer T3 anchor hit 4 z (t3_2_z)
+    float z5 = alpaka::math::abs(acc, mds.anchorZ()[mdIndex5]);  // outer T3 anchor hit 5 z (t3_4_z)
+
+    float r1 = mds.anchorRt()[mdIndex1];  // inner T3 anchor hit 1 r (t3_0_r)
+    float r2 = mds.anchorRt()[mdIndex2];  // inner T3 anchor hit 2 r (t3_2_r)
+    float r3 = mds.anchorRt()[mdIndex3];  // inner T3 anchor hit 3 r (t3_4_r)
+    float r4 = mds.anchorRt()[mdIndex4];  // outer T3 anchor hit 4 r (t3_2_r)
+    float r5 = mds.anchorRt()[mdIndex5];  // outer T3 anchor hit 5 r (t3_4_r)
+
+    // Build the input feature vector using pairwise differences after the first hit
+    float x[kinputFeatures] = {
+        eta1 / kEta_norm,                          // inner T3: First hit eta normalized
+        alpaka::math::abs(acc, phi1) / kPhi_norm,  // inner T3: First hit phi normalized
+        z1 / kZ_max,                               // inner T3: First hit z normalized
+        r1 / kR_max,                               // inner T3: First hit r normalized
+
+        eta2 - eta1,                        // inner T3: Difference in eta between hit 2 and 1
+        delta_phi(phi2, phi1) / kPhi_norm,  // inner T3: Difference in phi between hit 2 and 1
+        (z2 - z1) / kZ_max,                 // inner T3: Difference in z between hit 2 and 1 normalized
+        (r2 - r1) / kR_max,                 // inner T3: Difference in r between hit 2 and 1 normalized
+
+        eta3 - eta2,                        // inner T3: Difference in eta between hit 3 and 2
+        delta_phi(phi3, phi2) / kPhi_norm,  // inner T3: Difference in phi between hit 3 and 2
+        (z3 - z2) / kZ_max,                 // inner T3: Difference in z between hit 3 and 2 normalized
+        (r3 - r2) / kR_max,                 // inner T3: Difference in r between hit 3 and 2 normalized
+
+        eta4 - eta3,                        // outer T3: Difference in eta between hit 4 and 3
+        delta_phi(phi4, phi3) / kPhi_norm,  // inner T3: Difference in phi between hit 4 and 3
+        (z4 - z3) / kZ_max,                 // outer T3: Difference in z between hit 4 and 3 normalized
+        (r4 - r3) / kR_max,                 // outer T3: Difference in r between hit 4 and 3 normalized
+
+        eta5 - eta4,                        // outer T3: Difference in eta between hit 5 and 4
+        delta_phi(phi5, phi4) / kPhi_norm,  // inner T3: Difference in phi between hit 5 and 4
+        (z5 - z4) / kZ_max,                 // outer T3: Difference in z between hit 5 and 4 normalized
+        (r5 - r4) / kR_max,                 // outer T3: Difference in r between hit 5 and 4 normalized
+
+        alpaka::math::log10(acc, innerRadius),   // T5 inner radius (t5_innerRadius)
+        alpaka::math::log10(acc, bridgeRadius),  // T5 bridge radius (t5_bridgeRadius)
+        alpaka::math::log10(acc, outerRadius)    // T5 outer radius (t5_outerRadius)
+    };
+
+    float x_1[khiddenFeatures];  // Layer 1 output
+    float x_2[khiddenFeatures];  // Layer 2 output
+    float x_3[1];                // Layer 3 linear output
+
+    // Layer 1: Linear + Relu
+    linear_layer<kinputFeatures, khiddenFeatures>(x, x_1, wgtT_layer1, bias_layer1);
+    relu_activation<khiddenFeatures>(x_1);
+
+    // Layer 2: Linear + Relu
+    linear_layer<khiddenFeatures, khiddenFeatures>(x_1, x_2, wgtT_layer2, bias_layer2);
+    relu_activation<khiddenFeatures>(x_2);
+
+    // Layer 3: Linear + Sigmoid
+    linear_layer<khiddenFeatures, 1>(x_2, x_3, wgtT_output_layer, bias_output_layer);
+    float x_5 = sigmoid_activation(acc, x_3[0]);
+
+    // Get the bin index based on abs(eta) of first hit and t5_pt
+    float t5_pt = innerRadius * lst::k2Rinv1GeVf * 2;
+
+    uint8_t pt_index = (t5_pt > 5);
+    uint8_t bin_index = (eta1 > 2.5f) ? (kEtaBins - 1) : static_cast<unsigned int>(eta1 / 0.25f);
 
-  }  // namespace t5dnn
-}  // namespace ALPAKA_ACCELERATOR_NAMESPACE::lst
+    // Compare x_5 to the cut value for the relevant bin
+    return x_5 > kWp[pt_index][bin_index];
+  }
+}  // namespace ALPAKA_ACCELERATOR_NAMESPACE::lst::t5dnn
 
 #endif
diff --git a/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h b/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h
index d5321fea07a6e..42f7b19f33898 100644
--- a/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h
+++ b/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h
@@ -3,313 +3,255 @@
 
 #include <alpaka/alpaka.hpp>
 
-namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
-  namespace t5dnn {
+namespace ALPAKA_ACCELERATOR_NAMESPACE::lst::t5dnn {
+  ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_layer1[32] = {
+      -1.3837075f, -0.0653152f, -0.7900129f, 0.0714758f,  -1.1574365f, -1.4634879f, -0.9317133f, -0.1455518f,
+      -0.0459635f, -0.2055620f, 0.0586231f,  -0.8943899f, -0.1009487f, 0.0166031f,  -0.5451909f, -0.1384538f,
+      1.2664700f,  -1.8996916f, -0.0025585f, -0.1647783f, -1.9019107f, 0.0707104f,  -0.2373025f, 0.0357050f,
+      -0.0048417f, 2.3127339f,  -0.0508943f, -0.1116435f, -0.1610904f, -1.6463890f, -1.0739423f, -0.0962902f};
 
-    ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_0[32] = {
-        -4.5069356f, -5.8842053f, 1.0793180f,  -0.1540973f, -0.4705772f, 6.4027028f,  -0.6620818f, -7.0734525f,
-        0.6211641f,  4.9630723f,  3.4310920f,  -0.8856288f, 4.5843782f,  -6.0180559f, 0.0126438f,  -1.5725276f,
-        -0.8549317f, -6.8545237f, -1.2129461f, 3.0617838f,  -0.3911322f, 0.0799793f,  -2.5398655f, -0.5780622f,
-        2.8533990f,  -0.1777968f, -2.6457164f, -0.7976936f, 4.5644889f,  -2.1747942f, 3.4286616f,  -10.1073380f};
-    ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_0[38][32] = {
-        {6.1269712f,  -10.6625051f, 17.4907818f,  -0.0019928f, -3.4468415f,  1.6674044f,   -7.8957767f, 2.2077549f,
-         9.5517254f,  -5.1345053f,  -30.1643391f, 4.0148559f,  -19.8330841f, -18.3806915f, 0.1334764f,  1.6213616f,
-         -4.1423774f, -15.3062429f, -1.0209556f,  1.5580219f,  0.7426265f,   0.0033929f,   1.3924170f,  0.9196110f,
-         -0.8995734f, 1.0594707f,   39.4390869f,  8.7642002f,  28.4583893f,  -5.9235659f,  3.7221889f,  14.4167147f},
-        {1.7863803f,  -0.6068707f, 0.3166098f,  -0.0608759f, 0.5939785f, 0.4870262f,  -3.1375074f, -17.7147388f,
-         -0.7231818f, -9.3808413f, 2.2070611f,  15.7461920f, 0.9355862f, 2.3942475f,  -0.0671409f, 3.5954301f,
-         -3.0463996f, -2.0748904f, -0.5450584f, -4.4800100f, 0.6074556f, -0.0161482f, 3.0624702f,  -4.5688419f,
-         2.9881518f,  -0.3714012f, -0.0387531f, -0.7699140f, 4.4028845f, 5.0333014f,  -4.7350726f, -8.6568584f},
-        {5.6548429f,  -0.0207700f, 0.1785973f, 0.0881671f,  0.2530097f,   -0.1893259f, -0.1105739f, -0.5183877f,
-         1.0728362f,  0.1833011f,  1.7765219f, 0.3127359f,  0.0455277f,   -0.1442616f, -0.1048361f, -0.1235604f,
-         -0.1217661f, -0.5487315f, 0.7575656f, -0.1177454f, -17.0993137f, 0.1628031f,  0.2789381f,  0.5304270f,
-         0.0837841f,  -3.1120780f, 0.0074821f, -0.1648044f, -0.3395336f,  0.3958135f,  0.8718957f,  -1.1980486f},
-        {0.2401041f,  -0.0585765f, -0.0144584f, 0.0411095f,  0.0752229f, 0.0292672f,  -0.2437613f, -1.4396472f,
-         -0.0971315f, -1.7181139f, 0.2417643f,  2.2030578f,  0.0566049f, 0.1081589f,  -0.1060181f, 0.3473758f,
-         -0.7095683f, -0.0345675f, 0.2794849f,  -1.1702278f, 0.2622930f, -0.0072611f, 0.5026371f,  -1.2882922f,
-         -0.4712771f, 0.0597130f,  -0.0039970f, -0.6050836f, 0.1554724f, 1.0991164f,  -0.4975886f, 0.2597970f},
-        {0.0766028f,  0.0218421f,  -0.1739017f, -0.0076569f, 0.0384461f,  -0.1841756f, 0.9677940f,  -3.1114254f,
-         2.3830564f,  2.0706992f,  -0.9643140f, 0.7361387f,  -0.0060253f, -0.1554846f, -0.0831100f, 2.8754771f,
-         -1.4403527f, -0.5281797f, 0.5157787f,  4.2405987f,  0.4807618f,  0.0217647f,  -1.2626950f, 0.9145837f,
-         -0.3931780f, 0.3426280f,  -0.0065206f, -0.7510439f, -0.4555758f, 2.7724340f,  -1.2173026f, 0.1039017f},
-        {0.5685715f,  0.3927337f,  0.4942532f,  -0.0671033f, -0.2808350f, -0.0336000f, -1.3983957f, 0.9876546f,
-         -2.3840380f, 0.7315395f,  -2.2009561f, -1.4631602f, -0.4672308f, -0.4994236f, 0.1169335f,  -1.1894208f,
-         -1.2692982f, 0.3303853f,  -2.0147655f, -0.9912014f, 1.0042895f,  0.1121151f,  -1.0789106f, -2.2821584f,
-         -6.6459913f, -0.0959398f, -0.0068429f, -2.8177626f, 0.3213172f,  -2.6832986f, -4.7613306f, -0.9985733f},
-        {1.4419515f,  -0.3864825f, -0.6756768f, -0.1273375f, 0.4321181f, 0.3354745f,  -0.8236564f, -2.8190827f,
-         0.7090831f,  1.9072700f,  -3.1834064f, -2.6938572f, 0.5051147f, 1.4382831f,  0.1241910f,  -0.7352629f,
-         0.7703634f,  -1.7556250f, -2.1104112f, 3.0603442f,  1.9873468f, -0.0358815f, -1.0087154f, 3.8253262f,
-         -0.5466214f, 0.0875162f,  0.2691758f,  0.7121435f,  1.9314718f, -0.1580560f, 3.6484149f,  -5.3173709f},
-        {6.9104381f,  -0.0033664f, -1.4405546f, -0.1768288f, 0.2028089f,  -0.1012344f, -4.4735684f, 0.6354278f,
-         4.3039737f,  0.2056303f,  1.8338999f,  -1.1351355f, 0.1015760f,  -0.0733253f, -0.0561627f, 2.5292397f,
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-        {-2.1621978f, -4.7202382f, 1.7378219f,  0.1417439f,  -0.5000908f, 5.4468708f,  1.4260571f,  -6.6136570f,
-         1.5713804f,  3.4479704f,  2.7354901f,  -0.7388076f, 5.4666147f,  -3.8697338f, -0.1368596f, -2.7903373f,
-         -1.2043713f, -4.9554005f, 0.3324645f,  1.6767365f,  0.1156244f,  -0.0326964f, -2.0945346f, -0.4590589f,
-         3.0942657f,  0.0015020f,  -6.2626700f, -0.3969755f, 0.7717427f,  -1.9667094f, 2.9664171f,  -11.9477053f},
-    };
-    ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_2[32] = {
-        9.8383608f,  3.6922295f,  3.5774977f,  -4.4619012f, 6.5087032f,  -0.9540017f, -0.5059246f, 0.0706402f,
-        14.3396597f, -0.2771132f, -4.8409863f, -8.3581600f, -3.5078344f, 4.3287506f,  -5.7808843f, 3.9264839f,
-        -2.1697845f, -0.0040514f, -0.2095029f, -6.8678174f, 1.7911285f,  -0.4510343f, 1.2410443f,  -4.5678806f,
-        -0.5693849f, 2.3320096f,  4.4606552f,  -6.3771009f, -4.3149071f, -0.1905672f, -3.5726390f, -1.0744030f};
-    ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_2[32][32] = {
-        {-0.0155548f, 0.0243339f,  0.0037967f,  -0.2771824f, 0.0111955f,  -0.0115980f, 0.0079653f,  -2.9803498f,
-         -0.0061037f, -0.0956634f, 0.0332446f,  0.0179244f,  -0.0080377f, -9.0180779f, 0.1720033f,  0.0350694f,
-         -0.0146588f, -0.2135506f, -0.3158041f, 1.3697664f,  0.0119146f,  0.0119120f,  -0.0986927f, 0.0297492f,
-         0.0355827f,  -0.1196868f, -0.0745119f, 0.0281862f,  -0.0422190f, -0.3069138f, -0.0477367f, -0.0550450f},
-        {-1.7374619f, 1.4822800f,  -2.1885235f, 1.8354234f,  -0.5380136f, 1.6621803f,  0.6251035f,  0.1008954f,
-         -0.8387129f, -0.2063313f, 1.0661691f,  -0.9799694f, -5.1710258f, -3.2260630f, -1.5073707f, -1.0792168f,
-         1.8569958f,  -0.2289213f, 0.0563821f,  -1.6398847f, -4.1649504f, -2.7527378f, -0.0134577f, 3.0424533f,
-         0.0364320f,  0.6762254f,  -3.1551330f, 2.4888904f,  1.4757305f,  -0.3141717f, -2.0126467f, -0.1675602f},
-        {-0.9571826f, 0.0914152f,  0.0404339f,  0.2927902f,  0.2933607f,  0.0619171f,  0.0772318f,  -1.3796169f,
-         -0.8194544f, -0.2179988f, -1.1241078f, -0.1443964f, 0.0559355f,  -1.2914546f, -0.3445117f, 0.2031156f,
-         0.0273864f,  -0.0193422f, -0.2136522f, 0.0429592f,  0.0212854f,  0.0414394f,  -1.1734651f, 0.0582848f,
-         0.0136039f,  -0.1892604f, 0.0764908f,  -0.0130132f, -0.1272559f, -0.0818855f, -0.0408583f, -0.1563294f},
-        {-0.0213695f, 0.0596942f,  -0.0641309f, -0.0146449f, 0.0416586f,  -0.0378931f, 0.1234860f,  0.1622967f,
-         0.0794091f,  -0.0639933f, -0.1030663f, 0.0579078f,  0.1050275f,  -0.0136866f, 0.0149978f,  0.0876813f,
-         0.0693554f,  0.1612417f,  -0.0595916f, -0.1008234f, -0.0579058f, 0.0915138f,  0.1321436f,  -0.1484535f,
-         -0.0920316f, -0.0024532f, -0.1045300f, 0.0924260f,  0.0277524f,  -0.0287276f, -0.1271127f, 0.1164243f},
-        {0.0713067f,  0.0198056f,  -0.3023696f, -0.0025908f, -0.0085885f, -1.1157553f, 0.0236462f, -0.0704844f,
-         -0.0189257f, -0.0997382f, 0.3379845f,  -0.1229390f, -0.0616165f, -0.8968034f, 0.0401445f, -0.1144476f,
-         -0.0532077f, 0.0604580f,  0.0609454f,  -0.1613472f, 0.0103525f,  -0.1653874f, 0.0205189f, 0.0758978f,
-         -0.1514593f, 0.0151441f,  0.2043469f,  0.0349607f,  -0.1361278f, -0.1255922f, 0.0631648f, 0.3570991f},
-        {0.3371337f,  -3.7541580f, 2.2215877f,  -0.3390516f, 0.1912718f,  -4.1861577f, -1.2264019f, 2.8179801f,
-         0.0667294f,  -0.0093539f, 2.3029909f,  3.1814916f,  3.9780347f,  0.2310601f,  0.3986159f,  -0.8544636f,
-         0.4139664f,  -0.1876569f, -0.2448732f, -2.8053334f, 4.0488625f,  2.1094146f,  -6.7310257f, -4.9950023f,
-         -0.8315823f, 0.0555959f,  2.4573720f,  -3.7234364f, -4.2910552f, -0.2995245f, -3.2605181f, 2.3620574f},
-        {-1.5522735f,  -0.1866350f, -0.0067679f, 0.3196557f,  1.4052233f,  2.8143549f,  -0.9992948f, -0.5309914f,
-         -25.8852596f, -0.1218249f, 0.6625420f,  0.3007106f,  -0.2767264f, -0.1847300f, -0.5313534f, -0.0383462f,
-         -0.1987552f,  0.0581405f,  -0.3376078f, 1.2621028f,  0.0818709f,  -0.1401216f, -0.4550788f, -0.1592657f,
-         0.0597123f,   0.1344101f,  -0.1005317f, -0.1538406f, 2.9142656f,  -0.0806051f, -0.4267367f, -31.9512234f},
-        {0.6859627f,  0.1212986f,  0.1291616f,  0.0459838f,  -0.0899920f, 0.0287645f,  0.1987007f,  -2.7079368f,
-         -0.2628384f, -0.1402464f, -0.6302179f, -0.2923960f, -0.1106663f, 0.8256195f,  -2.8054097f, -0.0296494f,
-         -0.5632019f, -0.1335654f, -0.1558440f, -6.8611612f, 0.0203786f,  0.0046566f,  -0.4401442f, -0.0471430f,
-         0.4535986f,  -0.8657981f, 0.0684740f,  0.0518814f,  -0.0123748f, -0.2270164f, 0.0922878f,  -0.3863277f},
-        {0.0127175f,   2.3346109f,  -0.4390767f, -0.4657893f, 0.1659466f,  -0.1132782f, -0.4928388f, 0.7652873f,
-         1.1510741f,   -0.0879600f, 0.2721785f,  -0.1878961f, -0.3477249f, -0.8473209f, -0.8931856f, -0.4328294f,
-         -11.9181929f, -0.0282545f, -0.0217915f, 1.6676594f,  -0.2122232f, -0.6190930f, 1.9053432f,  -0.7592348f,
-         -1.0739189f,  -0.7170524f, 0.3864411f,  -0.8849231f, 0.1393488f,  0.0738489f,  0.4460345f,  1.9020857f},
-        {0.4453296f,  -0.0767821f, 0.1638939f, 1.6997167f,  -0.1098599f, -0.0551604f, 0.0040561f,  -13.5290670f,
-         -0.1285677f, -0.0590394f, 0.6499141f, -0.7617344f, 0.0453151f,  0.3104213f,  -1.0711143f, 0.1361838f,
-         -0.4365610f, -0.1300649f, 0.2013344f, -0.5308123f, 0.1451896f,  0.1030715f,  -0.6487910f, -0.3136590f,
-         -0.0280079f, 0.5394178f,  0.1318262f, -0.0159292f, 0.0636870f,  -0.3224248f, -0.1868187f, -0.2468304f},
-        {-0.0333494f, -0.0834255f, -0.1221875f, 0.6861304f,  0.0521738f,  -0.0416543f, -0.4437352f, -19.3246250f,
-         -0.1520821f, 0.0528602f,  -0.6375434f, -0.5803806f, -0.0958465f, -2.0058544f, -0.8282642f, 0.0259000f,
-         0.4846996f,  0.1211179f,  0.0356884f,  1.0009497f,  0.0635682f,  -0.0314105f, -0.0011147f, 0.0131714f,
-         -0.3410152f, 0.2798154f,  0.0961889f,  0.1266228f,  -0.0934717f, -0.0904307f, 0.1355542f,  0.5722573f},
-        {0.2146454f, 0.2143834f,  0.1290650f,  -0.9063646f, 0.2100945f,  0.1331054f,  -0.2620614f, -0.1264993f,
-         0.1313979f, 0.0455465f,  -0.8395286f, -0.4967833f, -0.0538581f, 0.9155380f,  0.6627046f,  0.1691243f,
-         0.9887002f, -0.1597013f, -0.1236713f, -1.9041336f, 0.0427585f,  0.0849747f,  -5.2559652f, -0.3133100f,
-         0.0141170f, -0.1635530f, 0.4938746f,  0.0162943f,  0.2107756f,  -0.3413893f, -0.0657575f, 1.0542560f},
-        {-2.8868380f, -2.0837426f, -1.0611480f, -0.6143807f, -0.6398501f, -2.8018746f, 0.5166737f,  -1.0814301f,
-         -1.9272422f, -0.1017482f, -0.4651161f, -1.4021232f, 1.8854499f,  0.1815407f,  0.5965426f,  -2.3344259f,
-         -0.0690846f, -0.1678239f, -0.4219488f, 0.6215640f,  1.0270095f,  -0.3473049f, -0.3926674f, -0.7942593f,
-         1.1305071f,  -1.4621233f, -0.8051161f, -0.7698632f, -2.6038630f, -0.3090037f, -1.6365144f, -1.0179478f},
-        {0.0046026f,  1.1319581f,  -2.6405678f, -2.0353596f, -2.1687336f, 0.3364883f,  2.1122196f,  0.2584647f,
-         -2.4344857f, -0.0378498f, 0.6158544f,  -0.6060749f, -4.9598379f, 0.1570698f,  2.2436838f,  -2.6198347f,
-         -2.0935996f, -0.1845744f, -0.0716080f, -1.9338604f, -4.1995640f, -3.6706774f, -1.6762524f, 3.9646862f,
-         -0.9677961f, 1.8319578f,  -3.1916575f, 3.7312632f,  0.0820446f,  -0.0497568f, -0.0898171f, -0.2499462f},
-        {-0.0780375f, -0.0286571f, 0.1007227f,  0.0012229f,  -0.0531285f, 0.0840718f,  0.1013894f,  0.1312424f,
-         -0.0673772f, 0.1603183f,  0.0074385f,  -0.0718321f, -0.1549873f, 0.1616689f,  0.0405887f,  -0.1558588f,
-         0.0740745f,  0.1696893f,  -0.0064026f, -0.1656420f, -0.1186674f, -0.1262667f, -0.0784757f, -0.1280154f,
-         0.0909976f,  0.0853046f,  -0.1075811f, 0.1310615f,  0.0610194f,  0.0647223f,  0.1360559f,  0.0440074f},
-        {-0.2106480f, 0.0087131f,  0.1119385f,  -1.0611318f, 0.5250220f,  0.0525479f,  -0.2733742f, -1.0799565f,
-         -0.5601607f, -0.0651806f, -1.9793440f, -0.3373334f, -0.1550518f, 0.8932216f,  0.7264332f,  -0.0450735f,
-         1.2373760f,  -0.1236272f, 0.0680048f,  -3.0446634f, -0.1533586f, -0.0127355f, -0.3326311f, -0.0225603f,
-         -0.2265739f, -2.3752897f, -0.3771705f, -0.0728938f, 0.1741305f,  0.1111639f,  0.4131119f,  0.2239323f},
-        {-2.5691276f, -1.4011253f, -2.0640867f, -3.7236946f, 1.5542637f,  -0.9456654f, -1.7575809f, 3.6794879f,
-         -0.4439790f, -0.1009826f, 3.6702275f,  -0.1935008f, -0.4423219f, -0.3825364f, -0.4784791f, 0.5927492f,
-         -2.3482494f, 0.0801714f,  -0.1567418f, -1.7934613f, -0.1706410f, -0.6326947f, 0.6260155f,  0.3631033f,
-         -0.9325932f, 1.9647995f,  -1.3409088f, 1.3501998f,  0.0367797f,  -0.1744210f, 1.8690013f,  -1.0737898f},
-        {-0.5934777f, 0.6232591f,  -0.3391055f, 0.2640936f, -0.2824444f, 0.4815128f,  0.6625078f,  -0.1103976f,
-         0.9555223f,  -0.0624896f, -0.6778919f, 0.1181502f, -0.5425385f, 0.7297349f,  -1.7261271f, -0.2917557f,
-         1.1873137f,  -0.2725933f, 0.0975242f,  1.7756181f, -0.5735835f, -0.4453230f, 0.9800369f,  0.9344145f,
-         -1.8692539f, 0.0120440f,  -0.7315661f, 0.6250805f, 0.3839143f,  -0.0376306f, 0.3816243f,  0.6059195f},
-        {0.5522162f,   -1.8043815f, -10.9379101f, 0.5719097f,  -0.2246755f, -1.4856353f,  0.4877502f,  0.7163438f,
-         -11.8135147f, -0.0180790f, -0.9928634f,  0.1107815f,  -0.0005064f, -0.3824990f,  -0.7453306f, -1.9909632f,
-         -7.4362645f,  -0.0245507f, -0.1815712f,  -3.5507584f, -0.0075889f, -11.0296011f, -1.1292133f, -0.0710276f,
-         0.5675677f,   0.2017778f,  -0.0684891f,  -0.0367653f, -1.6674192f, 0.0281711f,   -0.8356591f, -0.0447807f},
-        {0.2537312f,  -3.0178010f, -0.3493635f, 1.8573236f,  0.4017631f,  0.9912633f,  -0.8625028f, -0.7783228f,
-         -1.7815375f, -0.1204695f, 1.8551122f,  0.3344182f,  -0.2828701f, -1.3226960f, -1.4470471f, 0.2895959f,
-         0.6780876f,  -0.2010069f, 0.0425280f,  -2.1786852f, -0.1274053f, -0.2549899f, -0.2233993f, -0.1561645f,
-         -0.4640818f, 0.6375850f,  0.7733670f,  -0.2388286f, 1.0447853f,  -0.1503223f, 0.3823584f,  -13.8176088f},
-        {0.2575197f,  -2.2127593f, -0.0389457f, -0.0215759f, 0.1659477f,  -0.0097748f, -0.1935415f, -0.9091369f,
-         -0.1453371f, 0.0442428f,  -0.1206519f, 0.1435609f,  -0.0186047f, -5.0154042f, 0.0538177f,  0.0403250f,
-         0.0240955f,  0.0331080f,  0.0517951f,  0.7422639f,  0.0069818f,  0.0248351f,  -0.2205741f, -0.0082387f,
-         0.2043269f,  0.0459435f,  0.0876343f,  0.0140607f,  0.1056308f,  0.0062555f,  0.0184278f,  -0.5539715f},
-        {-0.0398742f, 0.1075264f,  0.1725024f,  -0.0755192f, -0.0360048f, 0.1325573f,  0.0903103f,  -0.0882263f,
-         0.1207692f,  0.0032722f,  0.0048489f,  -0.1257241f, 0.1450990f,  -0.0713558f, 0.1116815f,  0.1107689f,
-         -0.1447252f, 0.1581838f,  -0.0160124f, -0.0425587f, 0.1411217f,  0.0865060f,  -0.0643460f, -0.0431262f,
-         -0.1452804f, -0.0195101f, 0.1234572f,  0.0520887f,  0.1117576f,  -0.0751791f, 0.1511539f,  0.1224861f},
-        {0.7728126f,  2.3075340f,  -0.0385258f, -3.1270287f, 0.9414487f,  3.5251477f,  -0.8043440f, 0.7212446f,
-         -7.6850162f, -0.1609414f, -3.7687578f, -1.0751100f, -0.2052089f, 5.0728245f,  2.2835267f,  0.5930225f,
-         0.1303335f,  -0.1428799f, -0.3715075f, 0.5136011f,  -0.4755619f, -0.2192461f, -3.8696294f, -0.0062392f,
-         -1.3774812f, -0.0034140f, -1.5944362f, 0.9773729f,  3.2859125f,  -0.1616932f, -1.2785367f, -13.5732412f},
-        {0.5535743f,  0.1461481f,  -0.2218016f, -0.2971808f, -0.2169309f, 0.1564545f,  -0.0390397f, 1.1558976f,
-         -0.0119933f, -0.0774637f, 1.1907971f,  -0.5127968f, -0.0066028f, -1.6794037f, -0.3650940f, 0.2555613f,
-         -0.9488379f, 0.0449603f,  -0.1620417f, 0.1583214f,  0.0000908f,  0.0152763f,  -1.0660053f, -0.0139402f,
-         -1.7440189f, 0.2515209f,  0.3333162f,  0.1904725f,  0.1116094f,  -0.2287960f, -0.0007165f, -1.7047704f},
-        {-5.9897852f,  -0.1316296f, -0.0218074f, -0.4602887f, 0.3288545f,  -0.0882939f, -0.5929499f, 0.4294790f,
-         -0.0383545f,  0.0556869f,  0.1975944f,  0.1341491f,  0.0629570f,  -2.2742157f, 0.0175826f,  -0.1439869f,
-         -24.8701649f, -0.1582915f, -0.2460304f, -3.9643264f, 0.0863483f,  0.0180861f,  -0.2210452f, -0.0868723f,
-         -0.4175525f,  -0.8231756f, 0.0247534f,  -0.1473545f, -0.0021330f, -0.0410253f, -1.1944869f, -1.1523768f},
-        {0.1031547f,  -3.3402514f, -4.3636522f, -0.1534714f, -0.0622189f, 0.0374694f,  -0.0870097f, -4.1865788f,
-         -0.0555377f, 0.0252329f,  0.1339467f,  0.0461691f,  -0.0503090f, 0.0289890f,  -0.0095674f, -0.3289992f,
-         -0.0279080f, 0.0274977f,  -0.0903500f, 0.5610157f,  -0.0478177f, 0.4346960f,  0.4822784f,  -0.1058945f,
-         -0.2026870f, -0.0560638f, 0.0910069f,  -0.0818529f, 0.0819198f,  -0.0292193f, 0.3040628f,  -0.1275230f},
-        {-5.8789845f, -17.1114635f, -4.6755161f, 0.1016624f,  -0.8685016f, -0.3898779f, -2.3363957f, 0.1413794f,
-         -2.4254086f, -0.2171030f,  -0.0901150f, 0.7058705f,  0.4166250f,  -0.0231085f, -0.1789686f, -9.4244318f,
-         -0.6418229f, -0.0857969f,  0.1683681f,  -0.0310597f, -0.0247807f, -5.3748040f, -7.4730940f, 0.1019564f,
-         -1.2126822f, -0.3726285f,  -1.0287101f, 0.1803891f,  -0.2227769f, -0.0791530f, -0.0159770f, -1.4883354f},
-        {-17.9394970f, -0.5228514f, -11.3547935f, -0.0672671f, -2.0371394f, -0.9076943f, 2.4331825f,  -6.9409127f,
-         0.8286008f,   0.0208618f,  -0.8009814f,  1.2268484f,  0.1943726f,  -1.7297083f, -0.7668949f, -6.5505466f,
-         -0.6495168f,  -0.0404727f, -0.1260914f,  -3.5029383f, -0.0852898f, -2.9679556f, 1.6404767f,  -0.0251449f,
-         1.1460075f,   -0.7877688f, -0.0586593f,  -0.4741839f, -1.7420560f, 0.0295600f,  -2.3574052f, 0.0974777f},
-        {0.4443443f,  0.6384261f,  1.3317494f,  -1.0085982f, 0.9508762f, 1.3168396f,  -0.1862490f, -0.1801148f,
-         1.1106120f,  -0.0654911f, 0.1186706f,  -0.7198273f, 0.5449172f, -0.5886080f, 0.7504217f,  1.8046317f,
-         -0.1294390f, -0.1939137f, -0.2383934f, 0.4131435f,  0.6910310f, 1.2821866f,  -0.1088722f, -0.5660405f,
-         -0.1188610f, 0.0364403f,  0.3597929f,  -0.6409024f, 1.2114668f, -0.0212278f, 0.8423592f,  0.4848156f},
-        {-0.8772649f, -13.5265112f, -4.5540547f,  -0.2856667f, 0.7604876f,  -0.6829260f, -0.8320626f, 0.6541347f,
-         0.4020181f,  0.0009324f,   -10.9660740f, -0.3540186f, -0.2316812f, 0.3576394f,  0.0998953f,  -1.5738430f,
-         1.2089975f,  0.0706465f,   -0.2538019f,  0.7016497f,  -0.0282650f, -3.1291001f, -0.4375663f, -0.3979468f,
-         -0.1588882f, 0.3978875f,   0.2038192f,   -0.4281644f, -0.5787544f, -0.0922198f, 0.9595569f,  0.0212818f},
-        {0.3392667f,  0.1170919f,  -0.0705636f, -0.1025443f, -0.1192213f, -0.0495686f, 0.0284667f,  -0.1226804f,
-         0.0050191f,  -0.0516545f, -1.0892097f, 0.0033689f,  0.0471462f,  1.4266804f,  0.0288870f,  -0.0110408f,
-         -1.1283765f, -0.1299917f, -0.4318301f, -0.9854419f, -0.0190479f, -0.0269406f, 0.3697925f,  -0.0757695f,
-         -0.3632923f, -0.1714077f, 0.0669245f,  0.0557428f,  -0.1713906f, -0.4307863f, -0.1749060f, -2.1246362f},
-        {0.8383662f, -3.8122442f, 0.1568939f,  -2.2105119f, -0.7086993f, -0.4664145f, -0.3578597f, 0.5554636f,
-         0.6965880f, -0.1506968f, 0.2646832f,  0.2874083f,  0.1901203f,  -2.4997077f, -0.3519035f, -0.0518054f,
-         1.0862818f, -0.2502540f, -0.3133347f, -0.7411230f, 0.1268138f,  0.1069811f,  -0.8109779f, 0.0264679f,
-         0.1604289f, -0.7534032f, -0.1419461f, 0.0688303f,  -0.1570919f, -0.3055144f, -0.7415189f, 2.5547018f},
-    };
-    ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_4[1] = {1.4616280f};
-    ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_4[32][1] = {
-        {0.0609813f},  {0.0685224f}, {0.1655236f},  {-0.0599842f}, {0.0669006f},  {-0.1817371f}, {-0.0539167f},
-        {-0.0737955f}, {0.0654664f}, {0.0302955f},  {-0.0586768f}, {0.0717433f},  {0.1472274f},  {-0.0610073f},
-        {-0.0601061f}, {0.2086218f}, {-0.0545418f}, {-0.0388369f}, {-0.0613536f}, {-0.1141072f}, {-0.2289097f},
-        {-0.3354485f}, {0.0831025f}, {0.1333673f},  {0.0490410f},  {0.0484894f},  {0.0436755f},  {-0.1479877f},
-        {0.1540713f},  {0.0021261f}, {-0.0845848f}, {-0.0564973f},
-    };
+  ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_layer1[23][32] = {
+      {-0.1881404f, -0.0534256f, 1.6563641f,  0.0401664f,  2.8318353f,  1.5049738f,  1.4111555f, -0.2339872f,
+       0.0431970f,  0.1220361f,  -0.0450153f, -1.6025578f, 0.0394025f,  -0.3051167f, 1.9442217f, 0.1599094f,
+       0.1376955f,  2.4181051f,  -0.0226484f, -0.1801709f, -0.4861264f, -0.0268545f, 0.5463807f, 0.2420150f,
+       -0.1238829f, 0.2916382f,  0.1507791f,  0.7952659f,  0.2736979f,  3.2790639f,  1.2062043f, -0.0884467f},
+      {-0.0469924f, 0.2013927f,  0.0307775f,  -0.1241788f, -0.0100412f, 0.0422375f,  0.0211071f,  -0.0359304f,
+       0.0451861f,  0.0291862f,  -0.2094866f, -0.0013007f, 0.1191471f,  0.0750159f,  0.0184378f,  0.0419437f,
+       -0.0207304f, -0.0444109f, 0.0013400f,  -0.0699210f, -0.0668742f, -0.0880825f, -0.0107244f, 0.0363424f,
+       0.1391699f,  -0.0112885f, -0.0060098f, -0.0073863f, -0.0566143f, -0.0224207f, 0.0103718f,  -0.0015193f},
+      {0.4520382f,  0.1227609f,  -1.3887709f, -0.0542129f, -3.2003114f, -0.8354173f, -1.3173198f, 0.3292131f,
+       -0.1657729f, -0.1982902f, 0.1599589f,  -0.0417666f, -0.1461042f, -1.3237997f, -5.3609071f, -0.0981676f,
+       0.2922535f,  -1.8692241f, -0.0345302f, 0.1810613f,  0.4473544f,  -0.0159401f, -0.7293931f, -1.4816793f,
+       -0.1431545f, -0.0955672f, -0.2370718f, -0.7204540f, 0.8451244f,  -3.4310548f, -1.3518151f, 0.1551731f},
+      {0.2670300f,  0.1343590f,  3.0347505f,  -0.1783503f, 2.1586559f,  2.4137778f,  2.0080864f, -0.2545274f,
+       -0.1985905f, 0.1653812f,  -0.1714860f, 4.1022782f,  -0.1045471f, 4.4776497f,  3.3737848f, -0.0849546f,
+       -6.1899095f, 3.6970129f,  0.0007382f,  0.1675882f,  0.6014717f,  -0.0287709f, 0.0495882f, 2.2192705f,
+       -0.1043157f, -4.7508621f, -0.0022774f, 0.3766513f,  -0.7505829f, 1.9759512f,  1.6747239f, -0.1004091f},
+      {0.6639504f,  -0.0384022f, -10.0415087f, -0.0032648f, 0.3049855f,  -2.0427964f, -1.1522077f, 0.0935732f,
+       0.1232134f,  0.0868663f,  -0.0230848f,  -1.8257296f, -0.0799238f, 6.8892417f,  -1.3941933f, 0.0445172f,
+       0.9485117f,  -2.5238073f, -0.0148513f,  0.2256772f,  0.5914315f,  -0.1278037f, 0.1609928f,  11.3438406f,
+       -0.0831544f, 0.1928522f,  0.0361467f,   0.0137040f,  4.9549832f,  2.3954937f,  0.3917757f,  0.1206975f},
+      {29.6590214f, -0.0836848f, -1.3028307f, -0.1391431f, -0.3703596f,  5.3762760f,  1.8429571f,  21.0697041f,
+       -0.1232606f, 0.0066067f,  -0.0308768f, -0.9960231f, 0.1865301f,   -1.2142091f, 0.9273136f,  0.0974103f,
+       1.4067870f,  0.7268439f,  0.0035755f,  0.0619486f,  -32.8901024f, -0.1950644f, -0.3978897f, -3.1790049f,
+       -0.1371673f, 0.1569460f,  0.0268667f,  -0.4512640f, 0.3055371f,   -0.2241473f, -0.6455348f, 0.1178979f},
+      {-2.9178317f, -0.2023720f, -0.2946439f, -0.1851392f, -0.3493766f, -1.5397958f, -1.5902523f, 1.0981250f,
+       -0.1796725f, -0.0540953f, 0.0926500f,  2.0021629f,  -0.1277778f, 3.3643394f,  -7.5327554f, -0.0084912f,
+       2.7298651f,  0.2535582f,  0.0474618f,  -0.1377846f, -2.2746830f, -0.2016302f, -0.7150622f, 4.4011140f,
+       -0.1688751f, -1.2160714f, -0.0055839f, -1.1319760f, -2.2543004f, 0.6365916f,  -1.4942099f, -0.0992425f},
+      {-5.9751196f, -0.1597221f, -3.8946304f, 0.0537821f, 0.4741110f,  3.6895070f,  2.5116272f, 1.7058172f,
+       -0.0860321f, -0.1519644f, 0.1465356f,  1.4165760f, -0.0984433f, 1.6990343f,  4.0953226f, 0.1742475f,
+       -3.2570388f, 3.1653547f,  0.0135764f,  0.0092055f, -5.0966530f, -0.0542810f, 0.4907863f, 0.5900084f,
+       -0.1736992f, -4.9153452f, 0.2017547f,  0.2854181f, 3.1490057f,  0.2885774f,  0.9775900f, -0.2207156f},
+      {0.3805595f,   0.0308984f,  -9.5846119f, -0.0547350f, 1.9641919f,  2.0823991f,  9.9298115f,  0.0344243f,
+       -0.1557834f,  -0.1847700f, -0.1195207f, 4.4698248f,  0.1492174f,  0.4272707f,  4.7265644f,  0.0200772f,
+       -14.3444443f, 4.9532328f,  0.0319610f,  -0.0645846f, -0.6238102f, 0.1038110f,  0.2483765f,  -5.1799927f,
+       0.0782294f,   16.8777409f, 0.0196593f,  0.8423936f,  -8.5921221f, -0.0184179f, -5.7857180f, -0.0551181f},
+      {17.1570740f, 0.0265437f, -1.4766232f, -0.0528512f, 1.0128449f,   3.1529653f,  -0.6560294f, 8.7189465f,
+       -0.1728377f, 0.1245629f, 0.1072764f,  0.2649773f,  0.0254132f,   -0.8094708f, 1.8371828f,  0.1586192f,
+       1.9410020f,  0.9662392f, -0.0839922f, -0.2894930f, -16.5091496f, -0.1079556f, -0.1204132f, -0.9694697f,
+       0.0537786f,  0.2476868f, 0.0076408f,  0.1025890f,  0.1267423f,   0.4956081f,  0.1457323f,  0.1342634f},
+      {-0.5389574f, 0.1333421f,  -4.6338782f, -0.0645123f, -0.6526322f, -3.2958410f, -1.2309581f, -1.0803053f,
+       -0.1170542f, -0.0169311f, 0.1147491f,  2.9890807f,  -0.1234096f, 0.6792320f,  -3.9311285f, -0.0678321f,
+       -2.7922039f, 4.9413238f,  0.1060735f,  -0.1114068f, -2.2443752f, -0.1649915f, -0.3656403f, 2.5320942f,
+       -0.0249616f, -4.5098810f, -0.1773834f, -1.9516623f, -1.6839710f, -0.1365123f, 1.0296160f,  -0.0419825f},
+      {-2.4413636f, 0.1075683f,  -1.4518708f, 0.0537449f,  0.1154493f,  -0.5463845f, 1.3964951f, 2.6729572f,
+       -0.0206257f, 0.1435281f,  -0.1819518f, 0.4540120f,  -0.1910136f, 1.7696143f,  2.3670278f, 0.1324464f,
+       -0.5837788f, -2.2784615f, 0.0345478f,  -0.0980538f, -0.4999657f, 0.1178097f,  0.5756868f, -0.1058674f,
+       0.1920418f,  -3.5473657f, 0.2146371f,  0.2557987f,  1.3935618f,  0.3242345f,  0.2029733f, -0.1844350f},
+      {-0.9069599f, -0.2032758f,  -0.5786582f, 0.1395915f,  3.9338124f,  -1.6806563f,  0.4269728f,  -0.3697720f,
+       -0.0306356f, -0.0341866f,  -0.0635755f, 1.8898975f,  0.1968578f,  -17.2182655f, 1.4839698f,  -0.0541308f,
+       15.9838457f, 18.5951862f,  0.0078872f,  -0.1186571f, -2.4982276f, 0.0033835f,   0.3749593f,  -15.0238085f,
+       0.0595601f,  -16.8588371f, 0.1146287f,  0.1274172f,  19.3332062f, -7.0513921f,  -5.4852023f, 0.1681230f},
+      {-5.1457887f, 0.0335570f,  1.8620163f, 0.0560381f,  -0.6397949f, -4.0867515f, 1.3578068f,  -23.9992580f,
+       -0.1034287f, 0.1437906f,  0.1076568f, -0.6930848f, -0.1176134f, 2.2855785f,  -0.8021089f, 0.0424611f,
+       -0.6139123f, -3.1381547f, 0.0188163f, -0.1728741f, 0.6676420f,  -0.1124282f, 0.1077818f,  2.3839712f,
+       0.1340676f,  1.3538554f,  0.0421035f, 0.4513423f,  -0.1543196f, 0.5120541f,  -0.8940096f, -0.1175765f},
+      {2.1656792f,  0.1638565f,  4.5302448f,  0.0741160f,  3.3850696f,  -4.8867540f, 2.8059542f,  -0.0023008f,
+       -0.1248942f, -0.0075225f, -0.0082212f, -1.0955724f, -0.1462416f, -1.7098176f, -4.1775723f, 0.1950609f,
+       3.6847639f,  1.6520064f,  0.0310502f,  -0.0430167f, 3.4527576f,  0.1453262f,  -1.0126116f, 1.8785841f,
+       -0.0615105f, 1.0451943f,  -0.2653875f, -1.2223006f, -1.0100641f, 1.2076828f,  0.4882897f,  -0.0618375f},
+      {2.4578559f,  -0.1464199f, -1.3086185f, 0.1208716f, -0.2079897f, -2.7138259f, -1.4107026f, -0.4483974f,
+       -0.1599056f, 0.0242936f,  0.1326804f,  0.8664415f, 0.0588684f,  0.7366717f,  2.3159802f,  -0.1917707f,
+       -2.0800066f, -7.5100355f, 0.0585225f,  0.1582773f, 1.8128076f,  -0.0756957f, 0.8521049f,  0.5539182f,
+       -0.1738797f, -0.2020151f, 0.2219591f,  0.1088298f, -1.9535940f, 2.4130275f,  -0.0741222f, 0.1156681f},
+      {-0.4152933f, -0.0679605f, -0.5760314f, -0.0201883f, -14.1784763f, 0.7755737f,   -19.5469246f, 0.0381304f,
+       0.0160074f,  0.1124380f,  -0.0478151f, -2.3719466f, 0.0819727f,   -12.5069208f, 2.0468810f,   0.0964909f,
+       7.8784809f,  -6.3555703f, -0.0429914f, -0.0162720f, -0.9493829f,  0.0296786f,   -0.0244959f,  -12.6325788f,
+       -0.1871653f, -9.8338795f, 0.0391840f,  -0.1199073f, -11.7859421f, 8.7398720f,   19.4971046f,  -0.1954873f},
+      {-4.8962007f, -0.1695992f, 0.7760146f,  -0.0199836f, -0.0576061f, -6.0196476f, -2.3023551f, -20.0125084f,
+       -0.1957836f, -0.0993785f, 0.1109372f,  -0.0710161f, -0.0553650f, 0.2546394f,  -1.7578228f, 0.1498791f,
+       -2.6269529f, 1.3973731f,  0.0464059f,  -0.2307575f, 1.6730053f,  -0.0038867f, 0.1040150f,  2.6721606f,
+       0.2027777f,  -1.2358316f, -0.0587254f, 0.0610504f,  -0.1700777f, -0.4323797f, 1.0359807f,  -0.0127435f},
+      {1.1245984f,  -0.1806923f, -1.5868790f, 0.1536594f,  1.6837788f, -1.6474472f, -3.9225550f, 0.4506312f,
+       0.1854908f,  -0.1023232f, -0.0306957f, -0.8615071f, 0.0945480f, 2.0585704f,  0.6044773f,  0.1269336f,
+       2.4720187f,  -4.5123949f, -0.0657749f, 0.1738364f,  2.4188614f, 0.0038840f,  -0.2019601f, -0.3842189f,
+       -0.0493631f, 3.6777370f,  -0.1003436f, 0.6174496f,  1.0476112f, 2.7601521f,  0.9059890f,  -0.1691816f},
+      {1.9658293f,  0.2083382f,  1.7833723f, 0.0662620f,  -0.3932888f, -1.0642430f, 0.1807114f,  -1.1486723f,
+       -0.0177136f, -0.1706942f, 0.1730027f, 0.6712329f,  0.0485299f,  0.6379296f,  -0.2880911f, -0.1993632f,
+       -0.9471832f, 1.9425983f,  0.0328524f, 0.0777725f,  0.6454380f,  0.0143852f,  0.0192997f,  1.6793132f,
+       -0.1872064f, -1.5757623f, 0.0242778f, -0.5992475f, 2.2148299f,  -3.5215647f, -2.9748621f, 0.0112703f},
+      {0.3737165f,  0.0361593f,  -0.1075856f, -0.0312021f, -0.0786010f, 1.3149793f,  0.0237401f,  -0.0819654f,
+       -0.1388431f, -0.0306386f, -0.0704427f, -2.3997226f, -0.1392045f, 0.7729424f,  0.1253861f,  -0.0819755f,
+       -0.7590774f, -0.3295609f, -0.0172208f, -0.0551179f, 0.4599459f,  -0.1143881f, 2.7430685f,  0.3621114f,
+       -0.1475701f, 0.2296079f,  -2.2224922f, -0.9080986f, 0.2101683f,  0.1190262f,  -2.2205217f, -0.0811555f},
+      {0.3946800f, -0.1204188f, 0.0543225f,  -0.0392627f, 1.9454094f,  0.1865290f,  1.5276426f,  -0.0342965f,
+       0.0117116f, -0.1873923f, -0.1045035f, 1.8535231f,  -0.0207077f, 0.0981549f,  -0.0327459f, -0.1486938f,
+       0.6359531f, -0.1314566f, -2.1469448f, -0.1665767f, 0.5134121f,  -0.0341647f, -2.1786075f, -0.5976576f,
+       0.0111857f, 0.3272055f,  2.1917374f,  -1.6247722f, 1.6025572f,  -1.9965295f, 0.3347488f,  0.1113990f},
+      {0.0340557f,  -0.1659652f, -0.0042457f, 0.0010229f,  -2.1550148f, -0.4728722f, -1.3667214f, 0.2625635f,
+       -0.0302200f, -0.0322885f, 0.0227866f,  0.6977839f,  0.0050141f,  -1.6183628f, 0.0869662f,  -0.0775411f,
+       0.4754244f,  0.4596581f,  2.1509945f,  -0.0313832f, 0.0336208f,  -0.1547154f, -0.6017126f, 0.0369996f,
+       -0.1102583f, -0.5788267f, 0.0017006f,  2.6352038f,  -1.7847317f, 1.7510574f,  2.1478791f,  -0.2251654f},
+  };
 
-  }  // namespace t5dnn
-}  // namespace ALPAKA_ACCELERATOR_NAMESPACE::lst
+  ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_layer2[32] = {
+      -0.2689391f, 1.5461178f,  -0.2424639f, 0.4424149f,  -0.0411816f, -4.1070848f, 1.4709516f,  -0.2439820f,
+      -0.1750926f, 2.8802166f,  -0.1573734f, -1.3724055f, 0.3671952f,  1.8267332f,  1.5655776f,  -0.7323843f,
+      1.6318209f,  2.2198663f,  -1.5951139f, -0.0870247f, 0.2806863f,  -0.2407108f, 0.1310665f,  -0.5246177f,
+      0.1914421f,  -0.3386542f, -0.6310596f, 3.2995102f,  0.7519229f,  -0.1565450f, -0.1496341f, 1.0073272f};
+
+  ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_layer2[32][32] = {
+      {-0.1731049f, 1.7775618f,  -0.2532010f, -0.2902778f, -0.1392802f, 4.2428946f,  -0.1866968f, -0.1800365f,
+       -0.0634398f, 0.0763313f,  0.0472901f,  -0.8030146f, 0.3161853f,  -1.0713238f, -4.6514492f, -0.3908085f,
+       1.1607268f,  0.8834935f,  -0.1194544f, -0.0785166f, 0.4967587f,  -0.0558136f, -0.9601135f, -0.1001592f,
+       3.4427991f,  -0.2144053f, -0.3632556f, 0.0117088f,  0.1742481f,  -0.2540179f, -0.1705156f, -0.2627344f},
+      {-0.1478276f, -0.1659575f, 0.1602777f,  -0.0758106f, 0.1067696f,  -0.0247068f, -0.1123443f, -0.1724832f,
+       -0.0013103f, -0.0685904f, 0.1537329f,  0.1042632f,  -0.0360880f, -0.0679077f, 0.0672719f,  0.1597116f,
+       -0.0150259f, 0.0367102f,  -0.0545881f, -0.0693004f, -0.1008447f, -0.0672846f, -0.1395939f, -0.0324785f,
+       -0.1051702f, -0.0530534f, -0.1019061f, -0.0921245f, 0.1195077f,  0.0453448f,  0.0257045f,  -0.0622537f},
+      {-0.0363173f, -0.1990481f, -0.0452148f, 0.4074381f,  -0.0731660f, -0.0823270f, 0.3154473f,  -0.1909118f,
+       -0.0165690f, 0.1325824f,  -0.0760181f, 0.7768906f,  -0.2702211f, -0.6023573f, 1.5904741f,  0.2384946f,
+       0.7610655f,  -2.8705251f, 0.5754877f,  -0.1587478f, -0.5708794f, -0.3421216f, 0.5023443f,  1.2806857f,
+       0.2158970f,  -0.1364033f, -0.3398291f, 0.9066412f,  -1.2935438f, 0.0273695f,  -0.1850613f, -0.9301611f},
+      {-0.1281746f, 0.1695392f,  0.0805936f,  -0.0598281f, 0.1266985f,  -0.1697189f, -0.1091505f, -0.1569477f,
+       0.0363969f,  -0.0628394f, 0.0107523f,  0.0659535f,  -0.0568244f, -0.1299786f, 0.0005438f,  -0.0806242f,
+       -0.0806848f, -0.0919798f, -0.0748445f, 0.0792912f,  0.0022868f,  0.0211520f,  -0.0183716f, 0.1279848f,
+       -0.1518286f, -0.0113527f, 0.0824359f,  -0.0178597f, 0.0272009f,  0.0288935f,  0.0123459f,  0.1685353f},
+      {0.1099675f, -0.3914332f, -0.0647218f, -0.8259028f, -0.0283726f, -0.0860217f, -2.0489185f, 0.1042144f,
+       0.1024824f, 0.0735443f,  -0.1235109f, -3.3674469f, -0.1799957f, -7.1867313f, 1.6053666f,  -0.5203959f,
+       0.8686391f, -0.0675404f, -2.8893898f, -0.0796400f, 1.2672142f,  -0.0371844f, -1.8065344f, -2.2551982f,
+       0.0355568f, 0.0672171f,  0.7150316f,  1.3620002f,  -0.4106106f, 0.0126076f,  0.0408083f,  1.5958146f},
+      {0.0525989f,  1.8947815f,  -0.2513640f, -0.3715420f, -0.1752283f, 1.3911799f,  -0.7633898f, -0.1716654f,
+       -0.0145629f, -1.7601604f, -0.1943324f, -0.5716376f, -0.8281464f, -0.0308049f, -1.4709659f, -0.4294116f,
+       -0.1030817f, -0.1823493f, 0.7561242f,  -0.1608112f, 0.3980689f,  -0.2464017f, -1.3065518f, 0.0875702f,
+       -0.1504322f, -0.0352198f, -0.4051513f, 0.7010455f,  -0.2363433f, -0.1118084f, -0.1329087f, -0.3257700f},
+      {-0.1209070f, 0.1677164f,  -0.1353413f, -0.0410048f, -0.1432644f, 0.2649301f,  0.2247741f,  -0.0425357f,
+       -0.2644008f, 1.4204332f,  -0.2540753f, 0.2481354f,  1.9494507f,  -0.2003033f, -0.5938342f, -0.3314930f,
+       1.5038266f,  -2.4000788f, -1.6202501f, -0.0256936f, -0.2890913f, -0.2113032f, 0.9030544f,  1.1483711f,
+       0.0545346f,  -0.1961582f, -0.2267976f, 0.2372836f,  2.5995049f,  -0.1469661f, -0.1017130f, 1.6176132f},
+      {0.0542207f,  2.7658713f, -0.1700335f, -0.3357265f, -0.1097085f, 1.6508883f,  0.0132292f,  0.1211861f,
+       -0.0852982f, 0.9232512f, 0.0202751f,  0.3138782f,  0.2674713f,  0.1247260f,  0.3859081f,  0.3961721f,
+       1.0556988f,  0.8574673f, -0.1462571f, -0.1600272f, 0.4117427f,  -0.1561815f, 0.0553897f,  -0.2753994f,
+       5.8420453f,  0.0883128f, 0.3594444f,  -0.7174141f, 0.5683901f,  0.0096710f,  -0.0957449f, -0.0195320f},
+      {0.1561092f,  -0.0417566f, -0.1044470f, 0.1186895f,  -0.1195878f, 0.0446987f,  -0.1386125f, -0.0103878f,
+       0.1173026f,  0.1349312f,  -0.0676422f, -0.1452308f, 0.0093872f,  0.0069650f,  0.1739093f,  -0.1592752f,
+       -0.1329019f, -0.0459163f, -0.1511888f, -0.0040456f, 0.0065862f,  0.0106182f,  0.0318060f,  0.1003269f,
+       0.0249398f,  0.1661194f,  -0.0286407f, -0.1062361f, 0.0026465f,  -0.0091479f, -0.1493473f, 0.0519762f},
+      {-0.0702637f, 0.1154817f,  -0.0680643f, 0.1447217f,  0.1394082f,  -0.0691432f, 0.0939426f,  0.0483852f,
+       0.1437123f,  -0.1085759f, 0.0333924f,  -0.0683726f, 0.0707103f,  -0.0723069f, 0.0124601f,  -0.0309495f,
+       -0.0308395f, -0.0695953f, -0.1078720f, 0.0858701f,  -0.0773453f, 0.0477413f,  0.0615588f,  0.1656474f,
+       0.1718751f,  -0.1125762f, 0.1753366f,  -0.0557704f, 0.0921221f,  0.0372290f,  -0.1084552f, -0.0438967f},
+      {-0.0557757f, 0.0694144f,  0.1150911f, -0.0202319f, 0.0661389f,  -0.0928373f, 0.0441888f,  -0.0028318f,
+       -0.0039446f, 0.0294675f,  0.1353384f, 0.0427515f,  0.0695194f,  0.1329748f,  0.1339706f,  0.0713900f,
+       -0.1384726f, 0.0925476f,  0.1581103f, 0.0100842f,  -0.1248652f, -0.0173615f, 0.1637451f,  -0.0025173f,
+       -0.0331219f, -0.0335269f, 0.0949441f, 0.0538645f,  0.0834281f,  0.0137191f,  -0.1360130f, 0.0074489f},
+      {-0.0949665f, -0.2181539f, 0.0871969f,  3.0772011f,  -0.1152011f, -0.0022047f, 1.2700632f,  -0.1173392f,
+       -0.1678371f, -1.3448639f, -0.2893313f, 1.5105180f,  -0.6029126f, -1.1568675f, 1.4823192f,  0.1635401f,
+       -2.2136483f, -1.4164798f, -0.4795305f, -0.0807557f, -1.6675406f, -0.0992591f, 2.1212378f,  -0.9400231f,
+       -0.5339298f, -0.0342672f, -2.3564072f, 1.3407421f,  -3.8635128f, -0.1171367f, -0.0364181f, -3.2491686f},
+      {-0.1047117f, -0.0540412f, -0.1137928f, 0.1582367f,  -0.0982449f, 0.0511854f,  -0.0805884f, -0.1141258f,
+       0.0931992f,  -0.0227052f, 0.0780590f,  -0.1288135f, -0.1186576f, -0.0754066f, -0.1234059f, -0.0091936f,
+       0.0205475f,  0.1640417f,  -0.1527465f, 0.0068472f,  -0.1239804f, -0.0448335f, -0.0061169f, -0.0078998f,
+       0.0253047f,  0.0712901f,  0.0024753f,  -0.0259875f, -0.1238613f, 0.1096537f,  -0.0953007f, 0.1385384f},
+      {0.0521762f,  1.4885306f,  -0.1298001f, 2.3033395f,  -0.1589162f, -0.8458843f, 0.0631668f,  -0.1424429f,
+       -0.0384785f, 0.5599840f,  0.0008631f,  -1.5839294f, 1.9202064f,  0.6930331f,  0.4948464f,  -0.6195241f,
+       -3.0526664f, 3.1423819f,  -1.3433597f, -0.1167206f, -1.3491610f, -0.0901343f, -1.2291449f, 3.5039587f,
+       0.4674770f,  -0.3027362f, 0.8279622f,  0.3417586f,  0.1367343f,  -0.1085793f, -0.1048759f, 1.2729272f},
+      {-0.0029521f, 0.2439991f,  -0.0858953f, -2.7804739f, -0.0220416f, 0.0256599f,  -0.3304259f, -0.0586597f,
+       -0.0459698f, 0.1670698f,  -0.1359344f, -0.3957845f, -1.6954739f, 0.3318155f,  0.9375985f,  0.5211958f,
+       0.6071047f,  -3.4249072f, 1.3199407f,  0.0136374f,  1.2692807f,  0.0233104f,  -0.0731508f, 2.2171400f,
+       -0.6052189f, -0.0698463f, 1.6376522f,  -1.1908000f, -0.1706121f, -0.0380146f, 0.0144418f,  1.5177792f},
+      {-0.0314772f, 0.0523589f,  -0.0517322f, -0.0100344f, 0.0714635f,  -0.1646974f, 0.0800682f,  0.1132821f,
+       -0.0028872f, -0.1239987f, -0.1322138f, -0.1059789f, 0.1752418f,  0.0475279f,  -0.0046871f, 0.1574167f,
+       -0.0231106f, -0.0261228f, 0.0236005f,  0.1663371f,  0.1059707f,  0.1229704f,  0.1427562f,  -0.1648343f,
+       0.0992667f,  -0.0631751f, -0.1411413f, -0.0999486f, -0.0972435f, -0.1422556f, 0.0973614f,  -0.0156000f},
+      {-0.1309903f, -0.5060971f, -0.1911870f, 2.2349114f,  0.1010354f,  0.5538697f,  1.8757060f,  -0.1538645f,
+       -0.2073075f, -1.8350753f, 0.0532570f,  1.8151909f,  -0.6800886f, 0.2615838f,  -0.6204563f, -0.1238837f,
+       -0.4772464f, -2.4070835f, -0.2783994f, -0.0211087f, -4.4925098f, -0.0790045f, 1.3566529f,  -0.3650998f,
+       -0.4658130f, -0.0479139f, -1.9361999f, 2.1485121f,  -3.1108823f, -0.0020647f, -0.0489678f, -0.4781263f},
+      {-0.0099352f, -1.9572417f, 0.0918592f,  0.7327217f,  -0.0609625f, -0.1969659f, 0.1922992f,  -0.1091586f,
+       -0.2125459f, -1.9542989f, -0.1648019f, -0.9355955f, 0.9144324f,  -5.0530005f, -0.2265045f, -0.5638458f,
+       4.4370432f,  -2.0318019f, -1.5679311f, 0.0221776f,  -0.4063498f, -0.1160609f, 0.9651156f,  -0.2401051f,
+       0.1903293f,  -0.2355373f, 0.2334733f,  0.1025979f,  0.7150746f,  0.0315593f,  -0.0001765f, 0.0137871f},
+      {0.0320691f,  -1.8876421f, -0.1241799f,  -3.1652985f, -0.1528286f, 2.1882250f,  -2.5907574f, 0.0210803f,
+       -0.1545521f, 0.7706368f,  -0.1652040f,  -4.1518817f, 4.2974262f,  0.3074523f,  3.3711803f,  -37.9055862f,
+       1.0623894f,  0.4360786f,  -2.6417589f,  0.1113010f,  3.8902094f,  -0.1616735f, 0.5595753f,  1.5364015f,
+       -2.4740698f, -0.0240434f, -28.0232792f, 0.6092473f,  1.6978041f,  -0.0458809f, 0.0664777f,  0.2603019f},
+      {0.1044999f,  0.0054908f,  0.1407564f,  -0.1701076f, -0.1274551f, 0.0443607f,  0.1182709f,  -0.1103420f,
+       -0.1343671f, -0.0042888f, -0.1611361f, 0.0154269f,  0.2285106f,  0.0870507f,  0.0914433f,  0.0657276f,
+       -0.1664300f, -0.0342912f, 0.1037545f,  -0.1175308f, 0.1135652f,  0.1325845f,  -0.1459545f, -0.2156865f,
+       -0.1673723f, -0.1156510f, 0.0179541f,  0.0541515f,  0.0957617f,  -0.1297485f, 0.1045326f,  0.2950188f},
+      {-0.1401742f, -2.8181052f, -0.0588381f, -0.1517100f, -0.0608850f, -3.5837226f, -0.1528927f, -0.0211265f,
+       0.0881796f,  -0.4448619f, -0.1457623f, -0.8828475f, 0.1261238f,  -1.0495204f, -3.7918513f, -0.4645159f,
+       -0.0800092f, 0.0624971f,  0.1528609f,  -0.1069645f, 0.4319421f,  0.0651448f,  -0.6571375f, -0.0323338f,
+       -4.6534319f, -0.0538999f, -0.2221518f, 0.0972160f,  0.1496329f,  0.0570569f,  -0.1125795f, -0.0153687f},
+      {-0.1065502f, 0.0606179f,  -0.1400291f, -0.0220975f, -0.0613350f, -0.0038843f, -0.0132201f, 0.1678067f,
+       0.1008587f,  -0.1255144f, -0.0675021f, -0.0475353f, 0.0278098f,  0.0527470f,  -0.0089845f, -0.0622052f,
+       0.1088723f,  0.0053812f,  0.0627310f,  -0.0226460f, -0.1096366f, -0.0505830f, -0.0301058f, -0.0775778f,
+       -0.0008928f, -0.1157909f, 0.0544982f,  0.0430219f,  -0.0134386f, -0.1095094f, 0.1215172f,  0.0081556f},
+      {-0.1747307f, -0.7465636f,  -0.0497346f, -2.0686443f, 0.0190713f,  -2.9156351f, -5.4731860f, -0.0728399f,
+       -0.0845178f, -14.8429976f, -0.1068359f, 1.8549156f,  -3.1135283f, -0.0907917f, -0.0262453f, -8.8010912f,
+       -4.3007965f, -1.6772208f,  -0.2576891f, -0.0163111f, -7.8583646f, 0.0697906f,  -0.0943863f, -0.7450574f,
+       1.1493169f,  0.0921000f,   -0.2395420f, 0.5794312f,  -4.2405462f, -0.0910322f, -0.1381017f, -1.0270567f},
+      {-0.0446755f, -0.8131990f, -0.1741483f, -1.7555307f, 0.0153283f,  0.0734032f,  -0.5930048f, -0.0398877f,
+       -0.0215982f, 0.0497884f,  -0.0504920f, 0.0942539f,  -1.1370168f, -0.8821361f, -0.0879569f, 0.3811991f,
+       1.2224945f,  0.3782545f,  1.4800016f,  0.0494110f,  1.7101970f,  -0.2885793f, -0.1778114f, -1.3913733f,
+       -0.0944610f, -0.3578439f, 0.3491475f,  -3.0349872f, 0.8044587f,  0.0928676f,  -0.0395946f, 0.2008810f},
+      {0.0721043f,  -0.1181163f, 0.0108281f,  -0.1215726f, 0.1285277f, 0.0851443f,  0.0791321f,  0.1765833f,
+       -0.0324889f, -0.0150838f, -0.0051942f, 0.1685798f,  0.1521861f, 0.0283858f,  0.0326072f,  0.0346215f,
+       -0.1081120f, -0.0745824f, -0.1762613f, 0.0901582f,  0.1335704f, 0.1599123f,  -0.0097813f, 0.0364541f,
+       -0.0391450f, -0.0079635f, 0.1014886f,  0.0130333f,  0.0438304f, -0.0074333f, 0.0845035f,  -0.0471010f},
+      {0.0360538f,  -0.9701002f, -0.2217611f, -1.1626705f, 0.0548465f,  0.6605385f,  -0.6693703f, -0.1432099f,
+       -0.0754442f, -0.2380328f, -0.0754142f, -2.3242903f, 3.5773275f,  0.0707042f,  0.2052065f,  -1.3753067f,
+       -0.8530636f, 3.1850073f,  -0.2901604f, -0.1291050f, -4.4672642f, -0.2425279f, 0.1252670f,  0.4261391f,
+       -0.8620862f, 0.1153403f,  -0.1999598f, -4.7756801f, 2.8851914f,  -0.1340472f, 0.0482952f,  1.7996837f},
+      {-0.1654812f, 0.9604513f,  0.1770310f,   -16.5736618f, -0.0350192f, -0.5557595f, -35.3047371f, -0.1299658f,
+       0.0065243f,  -3.0823336f, 0.0351931f,   4.9456911f,   -1.4382623f, -1.6900688f, -1.9084880f,  -3.1811504f,
+       -8.0212736f, -7.3994560f, 4.9219728f,   0.0433824f,   0.6197430f,  0.0308996f,  5.2004323f,   0.5327767f,
+       1.0885966f,  0.1487215f,  -21.4211712f, -1.8733859f,  1.9195696f,  -0.0539309f, -0.0795544f,  -3.1121061f},
+      {-0.0058153f, 1.7521383f,  -0.2205407f, 2.6318321f,  -0.0038140f, -1.4131194f, 3.0181022f,  0.0373498f,
+       -0.1246315f, -1.8323456f, -0.1470954f, 2.9131169f,  1.1522563f,  0.6036215f,  -3.3962972f, 7.0906253f,
+       -1.5353408f, -0.2648884f, 0.5501783f,  -0.2262681f, -2.4874980f, -0.0533402f, 3.0222948f,  0.3296265f,
+       1.4057258f,  0.0185255f,  6.1208682f,  0.7210779f,  -0.3055671f, -0.2595702f, -0.1286864f, 0.6510819f},
+      {-0.2145578f, 0.4758183f,  -0.1186396f, -0.6096930f, -0.1574199f, -0.1929667f, -0.6877209f, -0.2098342f,
+       0.0726678f,  0.1379885f,  0.0710437f,  -1.1860796f, 0.6582619f,  0.2388466f,  0.0458675f,  -0.0634391f,
+       -0.1678368f, -8.2454395f, -0.6461441f, -0.2063597f, 0.0304686f,  0.0319904f,  -1.0730971f, 1.1281222f,
+       0.1292592f,  -0.3054110f, 0.7732272f,  -1.0069786f, -0.0847367f, -0.2342585f, -0.1553642f, 1.5100089f},
+      {-0.1022291f, 2.7367072f, -0.1738961f, -1.0328600f, -0.0864617f, -0.3224345f, -2.6092832f, -0.2382921f,
+       0.0578183f,  0.4115438f, 0.0121692f,  -1.0689495f, 0.5158959f,  2.9600139f,  0.8839240f,  -0.7147520f,
+       -2.7168157f, 1.2148006f, 1.5884653f,  -0.1227511f, 1.3176637f,  -0.1335970f, -1.4691980f, 1.1131358f,
+       -0.1302031f, 0.0779746f, 0.2622980f,  0.0837635f,  2.7756395f,  -0.0315265f, 0.0868374f,  -4.2980185f},
+      {0.0228074f,  2.1787968f,  -0.1889012f, -0.8560471f, -0.1063542f, -0.2869910f, 0.2767612f,  -0.1183861f,
+       -0.0992468f, 2.1517978f,  -0.0428540f, 1.0697522f,  1.9683092f,  2.1042306f,  -0.0426359f, -0.3499008f,
+       -0.9989156f, 0.0880459f,  2.9753070f,  -0.1941337f, -3.1616704f, -0.0093505f, 1.4922180f,  2.8480091f,
+       0.2656264f,  -0.1299839f, -1.0458518f, -1.6748481f, -3.1420829f, -0.1360553f, -0.1117443f, -1.3989290f},
+      {-0.0246332f, 0.1165779f,  0.0255498f,  -0.0601489f, 0.1545041f, -0.0977981f, 0.1242626f,  -0.1533627f,
+       -0.1294386f, -0.0231293f, -0.1460808f, 0.1763088f,  0.0953614f, -0.0716483f, -0.1003436f, 0.0804519f,
+       0.1373295f,  -0.0686773f, 0.1198382f,  0.1519430f,  0.1640775f, -0.1675753f, 0.0790529f,  -0.1521838f,
+       0.0378523f,  0.1039687f,  -0.0701027f, 0.0509319f,  0.1355647f, 0.0978021f,  0.0391430f,  0.0241266f},
+  };
+
+  ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_output_layer[1] = {-0.7420582f};
+
+  ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_output_layer[32][1] = {
+      {0.0381968f},  {1.0667214f},  {0.0505496f},  {-1.5677565f}, {0.0066824f},  {-0.9951485f}, {0.9438043f},
+      {0.0068631f},  {-0.0216870f}, {0.6560486f},  {-0.0235629f}, {0.9653404f},  {0.6641668f},  {-0.5351945f},
+      {-0.5303048f}, {1.9339687f},  {0.4359012f},  {-0.7492802f}, {-0.5728400f}, {0.0473893f},  {-0.5091293f},
+      {-0.1926489f}, {-0.6562935f}, {-0.5583456f}, {-0.7618014f}, {-0.0316967f}, {1.1637378f},  {-0.5158406f},
+      {-0.5268564f}, {0.0735416f},  {0.0270067f},  {-0.5614370f},
+  };
+
+}  // namespace ALPAKA_ACCELERATOR_NAMESPACE::lst::t5dnn
 
 #endif
diff --git a/RecoTracker/LSTCore/src/alpaka/PixelQuintuplet.h b/RecoTracker/LSTCore/src/alpaka/PixelQuintuplet.h
index 08feb0dfe3384..5ec314ef7bb1e 100644
--- a/RecoTracker/LSTCore/src/alpaka/PixelQuintuplet.h
+++ b/RecoTracker/LSTCore/src/alpaka/PixelQuintuplet.h
@@ -107,63 +107,61 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
         modules.layers()[lowerModuleIndex5] + 6 * (modules.subdets()[lowerModuleIndex5] == Endcap) +
         5 * (modules.subdets()[lowerModuleIndex5] == Endcap and modules.moduleType()[lowerModuleIndex5] == TwoS);
 
+    // These slides show the cut threshold definition. The comments below in the code, e.g, "cat 10", is consistent with the region separation in the slides
+    // https://indico.cern.ch/event/1410985/contributions/5931017/attachments/2875400/5035406/helix%20approxi%20for%20pT5%20rzchi2%20new%20results%20versions.pdf
     if (layer1 == 1 and layer2 == 2 and layer3 == 3) {
-      if (layer4 == 12 and layer5 == 13) {
-        return rzChiSquared < 451.141f;
-      } else if (layer4 == 4 and layer5 == 12) {
-        return rzChiSquared < 392.654f;
-      } else if (layer4 == 4 and layer5 == 5) {
-        return rzChiSquared < 225.322f;
-      } else if (layer4 == 7 and layer5 == 13) {
-        return rzChiSquared < 595.546f;
-      } else if (layer4 == 7 and layer5 == 8) {
-        return rzChiSquared < 196.111f;
+      if (layer4 == 12 and layer5 == 13) {  // cat 10
+        return rzChiSquared < 14.031f;
+      } else if (layer4 == 4 and layer5 == 12) {  // cat 12
+        return rzChiSquared < 8.760f;
+      } else if (layer4 == 4 and layer5 == 5) {  // cat 11
+        return rzChiSquared < 3.607f;
+      } else if (layer4 == 7 and layer5 == 13) {  // cat 9
+        return rzChiSquared < 16.620;
+      } else if (layer4 == 7 and layer5 == 8) {  // cat 8
+        return rzChiSquared < 17.910f;
       }
     } else if (layer1 == 1 and layer2 == 2 and layer3 == 7) {
-      if (layer4 == 13 and layer5 == 14) {
-        return rzChiSquared < 297.446f;
-      } else if (layer4 == 8 and layer5 == 14) {
-        return rzChiSquared < 451.141f;
-      } else if (layer4 == 8 and layer5 == 9) {
-        return rzChiSquared < 518.339f;
+      if (layer4 == 13 and layer5 == 14) {  // cat 7
+        return rzChiSquared < 8.950f;
+      } else if (layer4 == 8 and layer5 == 14) {  // cat 6
+        return rzChiSquared < 14.837f;
+      } else if (layer4 == 8 and layer5 == 9) {  // cat 5
+        return rzChiSquared < 18.519f;
       }
     } else if (layer1 == 1 and layer2 == 7 and layer3 == 8) {
-      if (layer4 == 9 and layer5 == 10) {
-        return rzChiSquared < 341.75f;
-      } else if (layer4 == 9 and layer5 == 15) {
-        return rzChiSquared < 341.75f;
+      if (layer4 == 9 and layer5 == 10) {  // cat 3
+        return rzChiSquared < 15.093f;
+      } else if (layer4 == 9 and layer5 == 15) {  // cat 4
+        return rzChiSquared < 11.200f;
       }
     } else if (layer1 == 2 and layer2 == 3 and layer3 == 4) {
-      if (layer4 == 12 and layer5 == 13) {
-        return rzChiSquared < 392.655f;
-      } else if (layer4 == 5 and layer5 == 12) {
-        return rzChiSquared < 341.75f;
-      } else if (layer4 == 5 and layer5 == 6) {
-        return rzChiSquared < 112.537f;
+      if (layer4 == 12 and layer5 == 13) {  // cat 20
+        return rzChiSquared < 12.868f;
+      } else if (layer4 == 5 and layer5 == 12) {  // cat 19
+        return rzChiSquared < 6.128f;
+      } else if (layer4 == 5 and layer5 == 6) {  // cat 18
+        return rzChiSquared < 2.987f;
       }
     } else if (layer1 == 2 and layer2 == 3 and layer4 == 7) {
-      if (layer4 == 13 and layer5 == 14) {
-        return rzChiSquared < 595.545f;
-      } else if (layer4 == 8 and layer5 == 14) {
-        return rzChiSquared < 74.198f;
+      if (layer4 == 13 and layer5 == 14) {  // cat 17
+        return rzChiSquared < 19.446f;
+      } else if (layer4 == 8 and layer5 == 14) {  // cat 16
+        return rzChiSquared < 17.520f;
       }
     } else if (layer1 == 2 and layer2 == 7 and layer3 == 8) {
-      if (layer4 == 14 and layer5 == 15) {
-        return rzChiSquared < 518.339f;
-      } else if (layer4 == 9 and layer5 == 10) {
-        return rzChiSquared < 8.046f;
-      } else if (layer4 == 9 and layer5 == 15) {
-        return rzChiSquared < 451.141f;
+      if (layer4 == 14 and layer5 == 15) {  // cat 15
+        return rzChiSquared < 14.71f;
+      } else if (layer4 == 9 and layer5 == 15) {  // cat 14
+        return rzChiSquared < 18.213f;
       }
-    } else if (layer1 == 3 and layer2 == 7 and layer3 == 8 and layer4 == 14 and layer5 == 15) {
-      return rzChiSquared < 56.207f;
     } else if (layer1 == 7 and layer2 == 8 and layer3 == 9) {
-      if (layer4 == 10 and layer5 == 11) {
-        return rzChiSquared < 64.578f;
-      } else if (layer4 == 10 and layer5 == 16) {
-        return rzChiSquared < 85.250f;
-      } else if (layer4 == 15 and layer5 == 16) {
-        return rzChiSquared < 85.250f;
+      if (layer4 == 10 and layer5 == 11) {  // cat 0
+        return rzChiSquared < 10.016f;
+      } else if (layer4 == 10 and layer5 == 16) {  // cat 1
+        return rzChiSquared < 87.671f;
+      } else if (layer4 == 15 and layer5 == 16) {  // cat 2
+        return rzChiSquared < 5.844f;
       }
     }
     return true;
@@ -254,138 +252,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     return true;
   }
 
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE float computeChiSquaredpT5(TAcc const& acc,
-                                                            unsigned int nPoints,
-                                                            float* xs,
-                                                            float* ys,
-                                                            float* delta1,
-                                                            float* delta2,
-                                                            float* slopes,
-                                                            bool* isFlat,
-                                                            float g,
-                                                            float f,
-                                                            float radius) {
-    /*
-    Given values of (g, f, radius) and a set of points (and its uncertainties) compute chi squared
-    */
-    float c = g * g + f * f - radius * radius;
-    float chiSquared = 0.f;
-    float absArctanSlope, angleM, xPrime, yPrime, sigma2;
-    for (size_t i = 0; i < nPoints; i++) {
-      absArctanSlope = ((slopes[i] != kVerticalModuleSlope) ? alpaka::math::abs(acc, alpaka::math::atan(acc, slopes[i]))
-                                                            : kPi / 2.f);
-      if (xs[i] > 0 and ys[i] > 0) {
-        angleM = kPi / 2.f - absArctanSlope;
-      } else if (xs[i] < 0 and ys[i] > 0) {
-        angleM = absArctanSlope + kPi / 2.f;
-      } else if (xs[i] < 0 and ys[i] < 0) {
-        angleM = -(absArctanSlope + kPi / 2.f);
-      } else if (xs[i] > 0 and ys[i] < 0) {
-        angleM = -(kPi / 2.f - absArctanSlope);
-      } else {
-        angleM = 0;
-      }
-      if (not isFlat[i]) {
-        xPrime = xs[i] * alpaka::math::cos(acc, angleM) + ys[i] * alpaka::math::sin(acc, angleM);
-        yPrime = ys[i] * alpaka::math::cos(acc, angleM) - xs[i] * alpaka::math::sin(acc, angleM);
-      } else {
-        xPrime = xs[i];
-        yPrime = ys[i];
-      }
-      sigma2 = 4 * ((xPrime * delta1[i]) * (xPrime * delta1[i]) + (yPrime * delta2[i]) * (yPrime * delta2[i]));
-      chiSquared += (xs[i] * xs[i] + ys[i] * ys[i] - 2 * g * xs[i] - 2 * f * ys[i] + c) *
-                    (xs[i] * xs[i] + ys[i] * ys[i] - 2 * g * xs[i] - 2 * f * ys[i] + c) / (sigma2);
-    }
-    return chiSquared;
-  }
-
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE void computeSigmasForRegression_pT5(TAcc const& acc,
-                                                                     ModulesConst modules,
-                                                                     const uint16_t* lowerModuleIndices,
-                                                                     float* delta1,
-                                                                     float* delta2,
-                                                                     float* slopes,
-                                                                     bool* isFlat,
-                                                                     unsigned int nPoints = 5,
-                                                                     bool anchorHits = true) {
-    /*
-    bool anchorHits required to deal with a weird edge case wherein
-    the hits ultimately used in the regression are anchor hits, but the
-    lower modules need not all be Pixel Modules (in case of PS). Similarly,
-    when we compute the chi squared for the non-anchor hits, the "partner module"
-    need not always be a PS strip module, but all non-anchor hits sit on strip
-    modules.
-    */
-    ModuleType moduleType;
-    short moduleSubdet, moduleSide;
-    float inv1 = kWidthPS / kWidth2S;
-    float inv2 = kPixelPSZpitch / kWidth2S;
-    float inv3 = kStripPSZpitch / kWidth2S;
-    for (size_t i = 0; i < nPoints; i++) {
-      moduleType = modules.moduleType()[lowerModuleIndices[i]];
-      moduleSubdet = modules.subdets()[lowerModuleIndices[i]];
-      moduleSide = modules.sides()[lowerModuleIndices[i]];
-      const float& drdz = modules.drdzs()[lowerModuleIndices[i]];
-      slopes[i] = modules.dxdys()[lowerModuleIndices[i]];
-      //category 1 - barrel PS flat
-      if (moduleSubdet == Barrel and moduleType == PS and moduleSide == Center) {
-        delta1[i] = inv1;
-        delta2[i] = inv1;
-        slopes[i] = -999.f;
-        isFlat[i] = true;
-      }
-      //category 2 - barrel 2S
-      else if (moduleSubdet == Barrel and moduleType == TwoS) {
-        delta1[i] = 1.f;
-        delta2[i] = 1.f;
-        slopes[i] = -999.f;
-        isFlat[i] = true;
-      }
-      //category 3 - barrel PS tilted
-      else if (moduleSubdet == Barrel and moduleType == PS and moduleSide != Center) {
-        delta1[i] = inv1;
-        isFlat[i] = false;
-
-        if (anchorHits) {
-          delta2[i] = (inv2 * drdz / alpaka::math::sqrt(acc, 1 + drdz * drdz));
-        } else {
-          delta2[i] = (inv3 * drdz / alpaka::math::sqrt(acc, 1 + drdz * drdz));
-        }
-      }
-      //category 4 - endcap PS
-      else if (moduleSubdet == Endcap and moduleType == PS) {
-        delta1[i] = inv1;
-        isFlat[i] = false;
-        /*
-        despite the type of the module layer of the lower module index,
-        all anchor hits are on the pixel side and all non-anchor hits are
-        on the strip side!
-        */
-        if (anchorHits) {
-          delta2[i] = inv2;
-        } else {
-          delta2[i] = inv3;
-        }
-      }
-      //category 5 - endcap 2S
-      else if (moduleSubdet == Endcap and moduleType == TwoS) {
-        delta1[i] = 1.f;
-        delta2[i] = 500.f * inv1;
-        isFlat[i] = false;
-      }
-#ifdef WARNINGS
-      else {
-        printf("ERROR!!!!! I SHOULDN'T BE HERE!!!! subdet = %d, type = %d, side = %d\n",
-               moduleSubdet,
-               moduleType,
-               moduleSide);
-      }
-#endif
-    }
-  }
-
   template <typename TAcc>
   ALPAKA_FN_ACC ALPAKA_FN_INLINE float computePT5RPhiChiSquared(TAcc const& acc,
                                                                 ModulesConst modules,
@@ -403,8 +269,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     bool isFlat[5];
     float chiSquared = 0;
 
-    computeSigmasForRegression_pT5(acc, modules, lowerModuleIndices, delta1, delta2, slopes, isFlat);
-    chiSquared = computeChiSquaredpT5(acc, 5, xs, ys, delta1, delta2, slopes, isFlat, g, f, radius);
+    computeSigmasForRegression(acc, modules, lowerModuleIndices, delta1, delta2, slopes, isFlat);
+    chiSquared = computeChiSquared(acc, 5, xs, ys, delta1, delta2, slopes, isFlat, g, f, radius);
 
     return chiSquared;
   }
@@ -512,28 +378,70 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
   template <typename TAcc>
   ALPAKA_FN_ACC ALPAKA_FN_INLINE float computePT5RZChiSquared(TAcc const& acc,
                                                               ModulesConst modules,
-                                                              uint16_t* lowerModuleIndices,
-                                                              float* rtPix,
-                                                              float* zPix,
-                                                              float* rts,
-                                                              float* zs) {
-    //use the two anchor hits of the pixel segment to compute the slope
-    //then compute the pseudo chi squared of the five outer hits
-
-    float slope = (zPix[1] - zPix[0]) / (rtPix[1] - rtPix[0]);
+                                                              const uint16_t* lowerModuleIndices,
+                                                              const float* rtPix,
+                                                              const float* xPix,
+                                                              const float* yPix,
+                                                              const float* zPix,
+                                                              const float* rts,
+                                                              const float* zs,
+                                                              float pixelSegmentPt,
+                                                              float pixelSegmentPx,
+                                                              float pixelSegmentPy,
+                                                              float pixelSegmentPz,
+                                                              int pixelSegmentCharge) {
     float residual = 0;
     float error2 = 0;
-    //hardcoded array indices!!!
     float RMSE = 0;
+
+    // the pixel positions are in unit of cm, and need to be divided by 100 to be in consistent with unit mm.
+    float Px = pixelSegmentPx, Py = pixelSegmentPy, Pz = pixelSegmentPz;
+    int charge = pixelSegmentCharge;
+    float x1 = xPix[1] / 100;
+    float y1 = yPix[1] / 100;
+    float z1 = zPix[1] / 100;
+    float r1 = rtPix[1] / 100;
+
+    float a = -100 / kR1GeVf * charge;
+
     for (size_t i = 0; i < Params_T5::kLayers; i++) {
-      uint16_t& lowerModuleIndex = lowerModuleIndices[i];
+      float zsi = zs[i] / 100;
+      float rtsi = rts[i] / 100;
+      uint16_t lowerModuleIndex = lowerModuleIndices[i];
       const int moduleType = modules.moduleType()[lowerModuleIndex];
       const int moduleSide = modules.sides()[lowerModuleIndex];
       const int moduleSubdet = modules.subdets()[lowerModuleIndex];
 
-      residual = (moduleSubdet == Barrel) ? (zs[i] - zPix[0]) - slope * (rts[i] - rtPix[0])
-                                          : (rts[i] - rtPix[0]) - (zs[i] - zPix[0]) / slope;
-      const float& drdz = modules.drdzs()[lowerModuleIndex];
+      // calculation is detailed documented here https://indico.cern.ch/event/1185895/contributions/4982756/attachments/2526561/4345805/helix%20pT3%20summarize.pdf
+      float diffr, diffz;
+      float p = alpaka::math::sqrt(acc, Px * Px + Py * Py + Pz * Pz);
+
+      float rou = a / p;
+      if (moduleSubdet == Endcap) {
+        float s = (zsi - z1) * p / Pz;
+        float x = x1 + Px / a * alpaka::math::sin(acc, rou * s) - Py / a * (1 - alpaka::math::cos(acc, rou * s));
+        float y = y1 + Py / a * alpaka::math::sin(acc, rou * s) + Px / a * (1 - alpaka::math::cos(acc, rou * s));
+        diffr = alpaka::math::abs(acc, rtsi - alpaka::math::sqrt(acc, x * x + y * y)) * 100;
+        residual = diffr;
+      }
+
+      if (moduleSubdet == Barrel) {
+        float paraA = r1 * r1 + 2 * (Px * Px + Py * Py) / (a * a) + 2 * (y1 * Px - x1 * Py) / a - rtsi * rtsi;
+        float paraB = 2 * (x1 * Px + y1 * Py) / a;
+        float paraC = 2 * (y1 * Px - x1 * Py) / a + 2 * (Px * Px + Py * Py) / (a * a);
+        float A = paraB * paraB + paraC * paraC;
+        float B = 2 * paraA * paraB;
+        float C = paraA * paraA - paraC * paraC;
+        float sol1 = (-B + alpaka::math::sqrt(acc, B * B - 4 * A * C)) / (2 * A);
+        float sol2 = (-B - alpaka::math::sqrt(acc, B * B - 4 * A * C)) / (2 * A);
+        float solz1 = alpaka::math::asin(acc, sol1) / rou * Pz / p + z1;
+        float solz2 = alpaka::math::asin(acc, sol2) / rou * Pz / p + z1;
+        float diffz1 = alpaka::math::abs(acc, solz1 - zsi) * 100;
+        float diffz2 = alpaka::math::abs(acc, solz2 - zsi) * 100;
+        diffz = alpaka::math::min(acc, diffz1, diffz2);
+        residual = diffz;
+      }
+
       //PS Modules
       if (moduleType == 0) {
         error2 = kPixelPSZpitch * kPixelPSZpitch;
@@ -544,6 +452,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
 
       //special dispensation to tilted PS modules!
       if (moduleType == 0 and moduleSubdet == Barrel and moduleSide != Center) {
+        float drdz = modules.drdzs()[lowerModuleIndex];
         error2 /= (1.f + drdz * drdz);
       }
       RMSE += (residual * residual) / error2;
@@ -571,7 +480,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                     float& quintupletRadius,
                                                                     float& centerX,
                                                                     float& centerY,
-                                                                    unsigned int pixelSegmentArrayIndex) {
+                                                                    unsigned int pixelSegmentArrayIndex,
+                                                                    const float ptCut) {
     unsigned int t5InnerT3Index = quintuplets.tripletIndices()[quintupletIndex][0];
     unsigned int t5OuterT3Index = quintuplets.tripletIndices()[quintupletIndex][1];
 
@@ -594,6 +504,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                        rzChiSquaredTemp,
                                        rPhiChiSquaredTemp,
                                        rPhiChiSquaredInwardsTemp,
+                                       ptCut,
                                        false))
       return false;
 
@@ -619,8 +530,10 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     uint16_t lowerModuleIndices[Params_T5::kLayers] = {
         lowerModuleIndex1, lowerModuleIndex2, lowerModuleIndex3, lowerModuleIndex4, lowerModuleIndex5};
 
-    float zPix[Params_pLS::kLayers] = {mds.anchorZ()[pixelInnerMDIndex], mds.anchorZ()[pixelOuterMDIndex]};
     float rtPix[Params_pLS::kLayers] = {mds.anchorRt()[pixelInnerMDIndex], mds.anchorRt()[pixelOuterMDIndex]};
+    float xPix[Params_pLS::kLayers] = {mds.anchorX()[pixelInnerMDIndex], mds.anchorX()[pixelOuterMDIndex]};
+    float yPix[Params_pLS::kLayers] = {mds.anchorY()[pixelInnerMDIndex], mds.anchorY()[pixelOuterMDIndex]};
+    float zPix[Params_pLS::kLayers] = {mds.anchorZ()[pixelInnerMDIndex], mds.anchorZ()[pixelOuterMDIndex]};
     float zs[Params_T5::kLayers] = {mds.anchorZ()[firstMDIndex],
                                     mds.anchorZ()[secondMDIndex],
                                     mds.anchorZ()[thirdMDIndex],
@@ -632,9 +545,32 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                      mds.anchorRt()[fourthMDIndex],
                                      mds.anchorRt()[fifthMDIndex]};
 
-    rzChiSquared = computePT5RZChiSquared(acc, modules, lowerModuleIndices, rtPix, zPix, rts, zs);
+    float pixelSegmentPt = segmentsPixel.ptIn()[pixelSegmentArrayIndex];
+    float pixelSegmentPx = segmentsPixel.px()[pixelSegmentArrayIndex];
+    float pixelSegmentPy = segmentsPixel.py()[pixelSegmentArrayIndex];
+    float pixelSegmentPz = segmentsPixel.pz()[pixelSegmentArrayIndex];
+    int pixelSegmentCharge = segmentsPixel.charge()[pixelSegmentArrayIndex];
+
+    rzChiSquared = 0;
 
-    if (/*pixelRadius*/ 0 < 5.0f * kR1GeVf) {  // FIXME: pixelRadius is not defined yet
+    //get the appropriate centers
+    pixelRadius = segmentsPixel.circleRadius()[pixelSegmentArrayIndex];
+
+    if (pixelRadius < 5.0f * kR1GeVf) {  //only apply r-z chi2 cuts for <5GeV tracks
+      rzChiSquared = computePT5RZChiSquared(acc,
+                                            modules,
+                                            lowerModuleIndices,
+                                            rtPix,
+                                            xPix,
+                                            yPix,
+                                            zPix,
+                                            rts,
+                                            zs,
+                                            pixelSegmentPt,
+                                            pixelSegmentPx,
+                                            pixelSegmentPy,
+                                            pixelSegmentPz,
+                                            pixelSegmentCharge);
       if (not passPT5RZChiSquaredCuts(modules,
                                       lowerModuleIndex1,
                                       lowerModuleIndex2,
@@ -657,10 +593,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                     mds.anchorY()[fourthMDIndex],
                                     mds.anchorY()[fifthMDIndex]};
 
-    //get the appropriate radii and centers
+    //get the appropriate centers
     centerX = segmentsPixel.circleCenterX()[pixelSegmentArrayIndex];
     centerY = segmentsPixel.circleCenterY()[pixelSegmentArrayIndex];
-    pixelRadius = segmentsPixel.circleRadius()[pixelSegmentArrayIndex];
 
     float T5CenterX = quintuplets.regressionG()[quintupletIndex];
     float T5CenterY = quintuplets.regressionF()[quintupletIndex];
@@ -679,8 +614,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
         return false;
     }
 
-    float xPix[] = {mds.anchorX()[pixelInnerMDIndex], mds.anchorX()[pixelOuterMDIndex]};
-    float yPix[] = {mds.anchorY()[pixelInnerMDIndex], mds.anchorY()[pixelOuterMDIndex]};
     rPhiChiSquaredInwards = computePT5RPhiChiSquaredInwards(T5CenterX, T5CenterY, quintupletRadius, xPix, yPix);
 
     if (quintuplets.regressionRadius()[quintupletIndex] < 5.0f * kR1GeVf) {
@@ -715,7 +648,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                   unsigned int* connectedPixelSize,
                                   unsigned int* connectedPixelIndex,
                                   unsigned int nPixelSegments,
-                                  ObjectRangesConst ranges) const {
+                                  ObjectRangesConst ranges,
+                                  const float ptCut) const {
       auto const globalBlockIdx = alpaka::getIdx<alpaka::Grid, alpaka::Blocks>(acc);
       auto const globalThreadIdx = alpaka::getIdx<alpaka::Grid, alpaka::Threads>(acc);
       auto const gridBlockExtent = alpaka::getWorkDiv<alpaka::Grid, alpaka::Blocks>(acc);
@@ -770,7 +704,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                          quintupletRadius,
                                                          centerX,
                                                          centerY,
-                                                         static_cast<unsigned int>(i_pLS));
+                                                         static_cast<unsigned int>(i_pLS),
+                                                         ptCut);
             if (success) {
               unsigned int totOccupancyPixelQuintuplets = alpaka::atomicAdd(
                   acc, &pixelQuintuplets.totOccupancyPixelQuintuplets(), 1u, alpaka::hierarchy::Threads{});
diff --git a/RecoTracker/LSTCore/src/alpaka/PixelTriplet.h b/RecoTracker/LSTCore/src/alpaka/PixelTriplet.h
index a8be90fff5227..cfd1b3d9b5a5c 100644
--- a/RecoTracker/LSTCore/src/alpaka/PixelTriplet.h
+++ b/RecoTracker/LSTCore/src/alpaka/PixelTriplet.h
@@ -10,6 +10,8 @@
 #include "RecoTracker/LSTCore/interface/SegmentsSoA.h"
 #include "RecoTracker/LSTCore/interface/TripletsSoA.h"
 
+#include "Quintuplet.h"
+
 namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
 
   template <typename TAcc>
@@ -27,7 +29,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                 unsigned int firstMDIndex,
                                                                 unsigned int secondMDIndex,
                                                                 unsigned int thirdMDIndex,
-                                                                unsigned int fourthMDIndex);
+                                                                unsigned int fourthMDIndex,
+                                                                const float ptCut);
   template <typename TAcc>
   ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runTripletDefaultAlgoPPEE(TAcc const& acc,
                                                                 ModulesConst modules,
@@ -43,7 +46,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                 unsigned int firstMDIndex,
                                                                 unsigned int secondMDIndex,
                                                                 unsigned int thirdMDIndex,
-                                                                unsigned int fourthMDIndex);
+                                                                unsigned int fourthMDIndex,
+                                                                const float ptCut);
 
   ALPAKA_FN_ACC ALPAKA_FN_INLINE void addPixelTripletToMemory(MiniDoubletsConst mds,
                                                               SegmentsConst segments,
@@ -121,7 +125,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                      uint16_t outerInnerLowerModuleIndex,
                                                                      uint16_t outerOuterLowerModuleIndex,
                                                                      unsigned int innerSegmentIndex,
-                                                                     unsigned int outerSegmentIndex) {
+                                                                     unsigned int outerSegmentIndex,
+                                                                     const float ptCut) {
     short outerInnerLowerModuleSubdet = modules.subdets()[outerInnerLowerModuleIndex];
     short outerOuterLowerModuleSubdet = modules.subdets()[outerOuterLowerModuleIndex];
 
@@ -147,7 +152,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                        firstMDIndex,
                                        secondMDIndex,
                                        thirdMDIndex,
-                                       fourthMDIndex);
+                                       fourthMDIndex,
+                                       ptCut);
     } else if (outerInnerLowerModuleSubdet == Endcap and outerOuterLowerModuleSubdet == Endcap) {
       return runTripletDefaultAlgoPPEE(acc,
                                        modules,
@@ -163,7 +169,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                        firstMDIndex,
                                        secondMDIndex,
                                        thirdMDIndex,
-                                       fourthMDIndex);
+                                       fourthMDIndex,
+                                       ptCut);
     }
     return false;
   }
@@ -219,52 +226,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     return true;
   }
 
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE float computeChiSquaredpT3(TAcc const& acc,
-                                                            unsigned int nPoints,
-                                                            float* xs,
-                                                            float* ys,
-                                                            float* delta1,
-                                                            float* delta2,
-                                                            float* slopes,
-                                                            bool* isFlat,
-                                                            float g,
-                                                            float f,
-                                                            float radius) {
-    //given values of (g, f, radius) and a set of points (and its uncertainties)
-    //compute chi squared
-    float c = g * g + f * f - radius * radius;
-    float chiSquared = 0.f;
-    float absArctanSlope, angleM, xPrime, yPrime, sigma2;
-    for (size_t i = 0; i < nPoints; i++) {
-      absArctanSlope = ((slopes[i] != kVerticalModuleSlope) ? alpaka::math::abs(acc, alpaka::math::atan(acc, slopes[i]))
-                                                            : kPi / 2.f);
-      if (xs[i] > 0 and ys[i] > 0) {
-        angleM = kPi / 2.f - absArctanSlope;
-      } else if (xs[i] < 0 and ys[i] > 0) {
-        angleM = absArctanSlope + kPi / 2.f;
-      } else if (xs[i] < 0 and ys[i] < 0) {
-        angleM = -(absArctanSlope + kPi / 2.f);
-      } else if (xs[i] > 0 and ys[i] < 0) {
-        angleM = -(kPi / 2.f - absArctanSlope);
-      } else {
-        angleM = 0;
-      }
-
-      if (not isFlat[i]) {
-        xPrime = xs[i] * alpaka::math::cos(acc, angleM) + ys[i] * alpaka::math::sin(acc, angleM);
-        yPrime = ys[i] * alpaka::math::cos(acc, angleM) - xs[i] * alpaka::math::sin(acc, angleM);
-      } else {
-        xPrime = xs[i];
-        yPrime = ys[i];
-      }
-      sigma2 = 4 * ((xPrime * delta1[i]) * (xPrime * delta1[i]) + (yPrime * delta2[i]) * (yPrime * delta2[i]));
-      chiSquared += (xs[i] * xs[i] + ys[i] * ys[i] - 2 * g * xs[i] - 2 * f * ys[i] + c) *
-                    (xs[i] * xs[i] + ys[i] * ys[i] - 2 * g * xs[i] - 2 * f * ys[i] + c) / sigma2;
-    }
-    return chiSquared;
-  }
-
   //TODO: merge this one and the pT5 function later into a single function
   template <typename TAcc>
   ALPAKA_FN_ACC ALPAKA_FN_INLINE float computePT3RPhiChiSquared(TAcc const& acc,
@@ -332,7 +293,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
       }
 #endif
     }
-    chiSquared = computeChiSquaredpT3(acc, 3, xs, ys, delta1, delta2, slopes, isFlat, g, f, radius);
+    chiSquared = computeChiSquared(acc, 3, xs, ys, delta1, delta2, slopes, isFlat, g, f, radius);
 
     return chiSquared;
   }
@@ -683,6 +644,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                  float& rzChiSquared,
                                                                  float& rPhiChiSquared,
                                                                  float& rPhiChiSquaredInwards,
+                                                                 const float ptCut,
                                                                  bool runChiSquaredCuts = true) {
     //run pT4 compatibility between the pixel segment and inner segment, and between the pixel and outer segment of the triplet
     uint16_t pixelModuleIndex = segments.innerLowerModuleIndices()[pixelSegmentIndex];
@@ -703,7 +665,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                              lowerModuleIndex,
                                              middleModuleIndex,
                                              pixelSegmentIndex,
-                                             triplets.segmentIndices()[tripletIndex][0]))
+                                             triplets.segmentIndices()[tripletIndex][0],
+                                             ptCut))
         return false;
 
       //pixel segment vs outer segment of triplet
@@ -717,7 +680,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                              middleModuleIndex,
                                              upperModuleIndex,
                                              pixelSegmentIndex,
-                                             triplets.segmentIndices()[tripletIndex][1]))
+                                             triplets.segmentIndices()[tripletIndex][1],
+                                             ptCut))
         return false;
     }
 
@@ -835,7 +799,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                   PixelTriplets pixelTriplets,
                                   unsigned int* connectedPixelSize,
                                   unsigned int* connectedPixelIndex,
-                                  unsigned int nPixelSegments) const {
+                                  unsigned int nPixelSegments,
+                                  const float ptCut) const {
       auto const globalBlockIdx = alpaka::getIdx<alpaka::Grid, alpaka::Blocks>(acc);
       auto const globalThreadIdx = alpaka::getIdx<alpaka::Grid, alpaka::Threads>(acc);
       auto const gridBlockExtent = alpaka::getWorkDiv<alpaka::Grid, alpaka::Blocks>(acc);
@@ -911,7 +876,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                       centerY,
                                                       rzChiSquared,
                                                       rPhiChiSquared,
-                                                      rPhiChiSquaredInwards);
+                                                      rPhiChiSquaredInwards,
+                                                      ptCut);
 
             if (success) {
               float phi =
@@ -962,105 +928,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     }
   };
 
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE void runDeltaBetaIterationspT3(TAcc const& acc,
-                                                                float& betaIn,
-                                                                float& betaOut,
-                                                                float betaAv,
-                                                                float& pt_beta,
-                                                                float sdIn_dr,
-                                                                float sdOut_dr,
-                                                                float dr,
-                                                                float lIn) {
-    if (lIn == 0) {
-      betaOut += alpaka::math::copysign(
-          acc,
-          alpaka::math::asin(
-              acc, alpaka::math::min(acc, sdOut_dr * k2Rinv1GeVf / alpaka::math::abs(acc, pt_beta), kSinAlphaMax)),
-          betaOut);
-      return;
-    }
-
-    if (betaIn * betaOut > 0.f and
-        (alpaka::math::abs(acc, pt_beta) < 4.f * kPt_betaMax or
-         (lIn >= 11 and alpaka::math::abs(acc, pt_beta) <
-                            8.f * kPt_betaMax)))  //and the pt_beta is well-defined; less strict for endcap-endcap
-    {
-      const float betaInUpd =
-          betaIn +
-          alpaka::math::copysign(
-              acc,
-              alpaka::math::asin(
-                  acc, alpaka::math::min(acc, sdIn_dr * k2Rinv1GeVf / alpaka::math::abs(acc, pt_beta), kSinAlphaMax)),
-              betaIn);  //FIXME: need a faster version
-      const float betaOutUpd =
-          betaOut +
-          alpaka::math::copysign(
-              acc,
-              alpaka::math::asin(
-                  acc, alpaka::math::min(acc, sdOut_dr * k2Rinv1GeVf / alpaka::math::abs(acc, pt_beta), kSinAlphaMax)),
-              betaOut);  //FIXME: need a faster version
-      betaAv = 0.5f * (betaInUpd + betaOutUpd);
-
-      //1st update
-      const float pt_beta_inv =
-          1.f / alpaka::math::abs(acc, dr * k2Rinv1GeVf / alpaka::math::sin(acc, betaAv));  //get a better pt estimate
-
-      betaIn += alpaka::math::copysign(
-          acc,
-          alpaka::math::asin(acc, alpaka::math::min(acc, sdIn_dr * k2Rinv1GeVf * pt_beta_inv, kSinAlphaMax)),
-          betaIn);  //FIXME: need a faster version
-      betaOut += alpaka::math::copysign(
-          acc,
-          alpaka::math::asin(acc, alpaka::math::min(acc, sdOut_dr * k2Rinv1GeVf * pt_beta_inv, kSinAlphaMax)),
-          betaOut);  //FIXME: need a faster version
-      //update the av and pt
-      betaAv = 0.5f * (betaIn + betaOut);
-      //2nd update
-      pt_beta = dr * k2Rinv1GeVf / alpaka::math::sin(acc, betaAv);  //get a better pt estimate
-    } else if (lIn < 11 && alpaka::math::abs(acc, betaOut) < 0.2f * alpaka::math::abs(acc, betaIn) &&
-               alpaka::math::abs(acc, pt_beta) < 12.f * kPt_betaMax)  //use betaIn sign as ref
-    {
-      const float pt_betaIn = dr * k2Rinv1GeVf / alpaka::math::sin(acc, betaIn);
-
-      const float betaInUpd =
-          betaIn +
-          alpaka::math::copysign(
-              acc,
-              alpaka::math::asin(
-                  acc, alpaka::math::min(acc, sdIn_dr * k2Rinv1GeVf / alpaka::math::abs(acc, pt_betaIn), kSinAlphaMax)),
-              betaIn);  //FIXME: need a faster version
-      const float betaOutUpd =
-          betaOut +
-          alpaka::math::copysign(
-              acc,
-              alpaka::math::asin(
-                  acc,
-                  alpaka::math::min(acc, sdOut_dr * k2Rinv1GeVf / alpaka::math::abs(acc, pt_betaIn), kSinAlphaMax)),
-              betaIn);  //FIXME: need a faster version
-      betaAv = (alpaka::math::abs(acc, betaOut) > 0.2f * alpaka::math::abs(acc, betaIn))
-                   ? (0.5f * (betaInUpd + betaOutUpd))
-                   : betaInUpd;
-
-      //1st update
-      pt_beta = dr * k2Rinv1GeVf / alpaka::math::sin(acc, betaAv);  //get a better pt estimate
-      betaIn += alpaka::math::copysign(
-          acc,
-          alpaka::math::asin(
-              acc, alpaka::math::min(acc, sdIn_dr * k2Rinv1GeVf / alpaka::math::abs(acc, pt_beta), kSinAlphaMax)),
-          betaIn);  //FIXME: need a faster version
-      betaOut += alpaka::math::copysign(
-          acc,
-          alpaka::math::asin(
-              acc, alpaka::math::min(acc, sdOut_dr * k2Rinv1GeVf / alpaka::math::abs(acc, pt_beta), kSinAlphaMax)),
-          betaIn);  //FIXME: need a faster version
-      //update the av and pt
-      betaAv = 0.5f * (betaIn + betaOut);
-      //2nd update
-      pt_beta = dr * k2Rinv1GeVf / alpaka::math::sin(acc, betaAv);  //get a better pt estimate
-    }
-  }
-
   template <typename TAcc>
   ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runTripletDefaultAlgoPPBB(TAcc const& acc,
                                                                 ModulesConst modules,
@@ -1076,7 +943,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                 unsigned int firstMDIndex,
                                                                 unsigned int secondMDIndex,
                                                                 unsigned int thirdMDIndex,
-                                                                unsigned int fourthMDIndex) {
+                                                                unsigned int fourthMDIndex,
+                                                                const float ptCut) {
     float dPhi, betaIn, betaOut, pt_beta, zLo, zHi, zLoPointed, zHiPointed, dPhiCut, betaOutCut;
 
     bool isPS_OutLo = (modules.moduleType()[outerInnerLowerModuleIndex] == PS);
@@ -1258,7 +1126,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
         alpaka::math::sqrt(acc, (x_OutUp - x_OutLo) * (x_OutUp - x_OutLo) + (y_OutUp - y_OutLo) * (y_OutUp - y_OutLo));
     float sdOut_d = rt_OutUp - rt_OutLo;
 
-    runDeltaBetaIterationspT3(acc, betaIn, betaOut, betaAv, pt_beta, rt_InSeg, sdOut_dr, drt_tl_axis, lIn);
+    runDeltaBetaIterations(acc, betaIn, betaOut, betaAv, pt_beta, rt_InSeg, sdOut_dr, drt_tl_axis, lIn);
 
     const float betaInMMSF = (alpaka::math::abs(acc, betaInRHmin + betaInRHmax) > 0)
                                  ? (2.f * betaIn / alpaka::math::abs(acc, betaInRHmin + betaInRHmax))
@@ -1334,7 +1202,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                 unsigned int firstMDIndex,
                                                                 unsigned int secondMDIndex,
                                                                 unsigned int thirdMDIndex,
-                                                                unsigned int fourthMDIndex) {
+                                                                unsigned int fourthMDIndex,
+                                                                const float ptCut) {
     float dPhi, betaIn, betaOut, pt_beta, rtLo, rtHi, dPhiCut, betaOutCut;
 
     bool isPS_OutLo = (modules.moduleType()[outerInnerLowerModuleIndex] == PS);
@@ -1517,7 +1386,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
         alpaka::math::sqrt(acc, (x_OutUp - x_OutLo) * (x_OutUp - x_OutLo) + (y_OutUp - y_OutLo) * (y_OutUp - y_OutLo));
     float sdOut_d = rt_OutUp - rt_OutLo;
 
-    runDeltaBetaIterationspT3(acc, betaIn, betaOut, betaAv, pt_beta, rt_InSeg, sdOut_dr, drt_tl_axis, lIn);
+    runDeltaBetaIterations(acc, betaIn, betaOut, betaAv, pt_beta, rt_InSeg, sdOut_dr, drt_tl_axis, lIn);
 
     const float betaInMMSF = (alpaka::math::abs(acc, betaInRHmin + betaInRHmax) > 0)
                                  ? (2.f * betaIn / alpaka::math::abs(acc, betaInRHmin + betaInRHmax))
diff --git a/RecoTracker/LSTCore/src/alpaka/Quintuplet.h b/RecoTracker/LSTCore/src/alpaka/Quintuplet.h
index 24ce2d1d53e22..679b1334038b2 100644
--- a/RecoTracker/LSTCore/src/alpaka/Quintuplet.h
+++ b/RecoTracker/LSTCore/src/alpaka/Quintuplet.h
@@ -2,6 +2,7 @@
 #define RecoTracker_LSTCore_src_alpaka_Quintuplet_h
 
 #include "HeterogeneousCore/AlpakaInterface/interface/workdivision.h"
+#include "FWCore/Utilities/interface/isFinite.h"
 
 #include "RecoTracker/LSTCore/interface/ObjectRangesSoA.h"
 #include "RecoTracker/LSTCore/interface/MiniDoubletsSoA.h"
@@ -18,13 +19,6 @@
 #include "Triplet.h"  // FIXME: need to refactor common functions to a common place
 
 namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool checkIntervalOverlap(float firstMin,
-                                                           float firstMax,
-                                                           float secondMin,
-                                                           float secondMax) {
-    return ((firstMin <= secondMin) && (secondMin < firstMax)) || ((secondMin < firstMin) && (firstMin < secondMax));
-  }
-
   ALPAKA_FN_ACC ALPAKA_FN_INLINE void addQuintupletToMemory(TripletsConst triplets,
                                                             Quintuplets quintuplets,
                                                             unsigned int innerTripletIndex,
@@ -43,6 +37,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                             float rzChiSquared,
                                                             float rPhiChiSquared,
                                                             float nonAnchorChiSquared,
+                                                            float dBeta1,
+                                                            float dBeta2,
                                                             float pt,
                                                             float eta,
                                                             float phi,
@@ -89,89 +85,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     quintuplets.rzChiSquared()[quintupletIndex] = rzChiSquared;
     quintuplets.chiSquared()[quintupletIndex] = rPhiChiSquared;
     quintuplets.nonAnchorChiSquared()[quintupletIndex] = nonAnchorChiSquared;
-  }
-
-  //90% constraint
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool passChiSquaredConstraint(ModulesConst modules,
-                                                               uint16_t lowerModuleIndex1,
-                                                               uint16_t lowerModuleIndex2,
-                                                               uint16_t lowerModuleIndex3,
-                                                               uint16_t lowerModuleIndex4,
-                                                               uint16_t lowerModuleIndex5,
-                                                               float chiSquared) {
-    // Using lstLayer numbering convention defined in ModuleMethods.h
-    const int layer1 = modules.lstLayers()[lowerModuleIndex1];
-    const int layer2 = modules.lstLayers()[lowerModuleIndex2];
-    const int layer3 = modules.lstLayers()[lowerModuleIndex3];
-    const int layer4 = modules.lstLayers()[lowerModuleIndex4];
-    const int layer5 = modules.lstLayers()[lowerModuleIndex5];
-
-    if (layer1 == 7 and layer2 == 8 and layer3 == 9) {
-      if (layer4 == 10 and layer5 == 11) {
-        return chiSquared < 0.01788f;
-      } else if (layer4 == 10 and layer5 == 16) {
-        return chiSquared < 0.04725f;
-      } else if (layer4 == 15 and layer5 == 16) {
-        return chiSquared < 0.04725f;
-      }
-    } else if (layer1 == 1 and layer2 == 7 and layer3 == 8) {
-      if (layer4 == 9 and layer5 == 10) {
-        return chiSquared < 0.01788f;
-      } else if (layer4 == 9 and layer5 == 15) {
-        return chiSquared < 0.08234f;
-      }
-    } else if (layer1 == 1 and layer2 == 2 and layer3 == 7) {
-      if (layer4 == 8 and layer5 == 9) {
-        return chiSquared < 0.02360f;
-      } else if (layer4 == 8 and layer5 == 14) {
-        return chiSquared < 0.07167f;
-      } else if (layer4 == 13 and layer5 == 14) {
-        return chiSquared < 0.08234f;
-      }
-    } else if (layer1 == 1 and layer2 == 2 and layer3 == 3) {
-      if (layer4 == 7 and layer5 == 8) {
-        return chiSquared < 0.01026f;
-      } else if (layer4 == 7 and layer5 == 13) {
-        return chiSquared < 0.06238f;
-      } else if (layer4 == 12 and layer5 == 13) {
-        return chiSquared < 0.06238f;
-      }
-    } else if (layer1 == 1 and layer2 == 2 and layer3 == 3 and layer4 == 4) {
-      if (layer5 == 5) {
-        return chiSquared < 0.04725f;
-      } else if (layer5 == 12) {
-        return chiSquared < 0.09461f;
-      }
-    } else if (layer1 == 2 and layer2 == 7 and layer3 == 8) {
-      if (layer4 == 9 and layer5 == 10) {
-        return chiSquared < 0.00512f;
-      }
-      if (layer4 == 9 and layer5 == 15) {
-        return chiSquared < 0.04112f;
-      } else if (layer4 == 14 and layer5 == 15) {
-        return chiSquared < 0.06238f;
-      }
-    } else if (layer1 == 2 and layer2 == 3 and layer3 == 7) {
-      if (layer4 == 8 and layer5 == 14) {
-        return chiSquared < 0.07167f;
-      } else if (layer4 == 13 and layer5 == 14) {
-        return chiSquared < 0.06238f;
-      }
-    } else if (layer1 == 2 and layer2 == 3 and layer3 == 4) {
-      if (layer4 == 5 and layer5 == 6) {
-        return chiSquared < 0.08234f;
-      } else if (layer4 == 5 and layer5 == 12) {
-        return chiSquared < 0.10870f;
-      } else if (layer4 == 12 and layer5 == 13) {
-        return chiSquared < 0.10870f;
-      }
-    } else if (layer1 == 3 and layer2 == 7 and layer3 == 8 and layer4 == 14 and layer5 == 15) {
-      return chiSquared < 0.09461f;
-    } else if (layer1 == 3 and layer2 == 4 and layer3 == 5 and layer4 == 12 and layer5 == 13) {
-      return chiSquared < 0.09461f;
-    }
-
-    return true;
+    quintuplets.dBeta1()[quintupletIndex] = dBeta1;
+    quintuplets.dBeta2()[quintupletIndex] = dBeta2;
   }
 
   //bounds can be found at http://uaf-10.t2.ucsd.edu/~bsathian/SDL/T5_RZFix/t5_rz_thresholds.txt
@@ -413,9 +328,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
         float solz2 = alpaka::math::asin(acc, sol2) / rou * Pz / p + z_init;
         float diffz1 = (solz1 - zsi) * 100;
         float diffz2 = (solz2 - zsi) * 100;
-        if (alpaka::math::isnan(acc, diffz1))
+        if (edm::isNotFinite(diffz1))
           diffz = diffz2;
-        else if (alpaka::math::isnan(acc, diffz2))
+        else if (edm::isNotFinite(diffz2))
           diffz = diffz1;
         else {
           diffz = (alpaka::math::abs(acc, diffz1) < alpaka::math::abs(acc, diffz2)) ? diffz1 : diffz2;
@@ -460,7 +375,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     }
     // for set rzchi2 cut
     // if the 5 points are linear, helix calculation gives nan
-    if (inner_pt > 100 || alpaka::math::isnan(acc, rzChiSquared)) {
+    if (inner_pt > 100 || edm::isNotFinite(rzChiSquared)) {
       float slope;
       if (moduleType1 == 0 and moduleType2 == 0 and moduleType3 == 1)  //PSPS2S
       {
@@ -622,251 +537,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     return (innerOuterOuterMiniDoubletIndex == outerInnerInnerMiniDoubletIndex);
   }
 
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE void computeErrorInRadius(TAcc const& acc,
-                                                           float* x1Vec,
-                                                           float* y1Vec,
-                                                           float* x2Vec,
-                                                           float* y2Vec,
-                                                           float* x3Vec,
-                                                           float* y3Vec,
-                                                           float& minimumRadius,
-                                                           float& maximumRadius) {
-    //brute force
-    float candidateRadius;
-    float g, f;
-    minimumRadius = kVerticalModuleSlope;
-    maximumRadius = 0.f;
-    for (size_t i = 0; i < 3; i++) {
-      float x1 = x1Vec[i];
-      float y1 = y1Vec[i];
-      for (size_t j = 0; j < 3; j++) {
-        float x2 = x2Vec[j];
-        float y2 = y2Vec[j];
-        for (size_t k = 0; k < 3; k++) {
-          float x3 = x3Vec[k];
-          float y3 = y3Vec[k];
-          candidateRadius = computeRadiusFromThreeAnchorHits(acc, x1, y1, x2, y2, x3, y3, g, f);
-          maximumRadius = alpaka::math::max(acc, candidateRadius, maximumRadius);
-          minimumRadius = alpaka::math::min(acc, candidateRadius, minimumRadius);
-        }
-      }
-    }
-  }
-
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool matchRadiiBBBEE12378(TAcc const& acc,
-                                                           float innerRadius,
-                                                           float bridgeRadius,
-                                                           float outerRadius,
-                                                           float bridgeRadiusMin2S,
-                                                           float bridgeRadiusMax2S) {
-    float innerInvRadiusMin, innerInvRadiusMax, bridgeInvRadiusMin, bridgeInvRadiusMax;
-
-    float innerInvRadiusErrorBound = 0.178f;
-    float bridgeInvRadiusErrorBound = 0.507f;
-
-    innerInvRadiusMax = (1.f + innerInvRadiusErrorBound) / innerRadius;
-    innerInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - innerInvRadiusErrorBound) / innerRadius);
-
-    bridgeInvRadiusMax = (1.f + bridgeInvRadiusErrorBound) / bridgeRadius;
-    bridgeInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - bridgeInvRadiusErrorBound) / bridgeRadius);
-
-    return checkIntervalOverlap(innerInvRadiusMin,
-                                innerInvRadiusMax,
-                                alpaka::math::min(acc, bridgeInvRadiusMin, 1.0f / bridgeRadiusMax2S),
-                                alpaka::math::max(acc, bridgeInvRadiusMax, 1.0f / bridgeRadiusMin2S));
-  }
-
-  /*bounds for high Pt taken from : http://uaf-10.t2.ucsd.edu/~bsathian/SDL/T5_efficiency/efficiencies/new_efficiencies/efficiencies_20210513_T5_recovering_high_Pt_efficiencies/highE_radius_matching/highE_bounds.txt */
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool matchRadiiBBBBB(TAcc const& acc,
-                                                      float innerRadius,
-                                                      float bridgeRadius,
-                                                      float outerRadius) {
-    float innerInvRadiusMin, innerInvRadiusMax, bridgeInvRadiusMin, bridgeInvRadiusMax;
-
-    float innerInvRadiusErrorBound = 0.1512f;
-    float bridgeInvRadiusErrorBound = 0.1781f;
-
-    if (innerRadius * k2Rinv1GeVf > 1.f) {
-      innerInvRadiusErrorBound = 0.4449f;
-      bridgeInvRadiusErrorBound = 0.4033f;
-    }
-
-    innerInvRadiusMax = (1.f + innerInvRadiusErrorBound) / innerRadius;
-    innerInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - innerInvRadiusErrorBound) / innerRadius);
-
-    bridgeInvRadiusMax = (1.f + bridgeInvRadiusErrorBound) / bridgeRadius;
-    bridgeInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - bridgeInvRadiusErrorBound) / bridgeRadius);
-
-    return checkIntervalOverlap(innerInvRadiusMin, innerInvRadiusMax, bridgeInvRadiusMin, bridgeInvRadiusMax);
-  }
-
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool matchRadiiBBBBE(TAcc const& acc,
-                                                      float innerRadius,
-                                                      float bridgeRadius,
-                                                      float outerRadius) {
-    float innerInvRadiusMin, innerInvRadiusMax, bridgeInvRadiusMin, bridgeInvRadiusMax;
-
-    float innerInvRadiusErrorBound = 0.1781f;
-    float bridgeInvRadiusErrorBound = 0.2167f;
-
-    if (innerRadius * k2Rinv1GeVf > 1.f) {
-      innerInvRadiusErrorBound = 0.4750f;
-      bridgeInvRadiusErrorBound = 0.3903f;
-    }
-
-    innerInvRadiusMax = (1.f + innerInvRadiusErrorBound) / innerRadius;
-    innerInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - innerInvRadiusErrorBound) / innerRadius);
-
-    bridgeInvRadiusMax = (1.f + bridgeInvRadiusErrorBound) / bridgeRadius;
-    bridgeInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - bridgeInvRadiusErrorBound) / bridgeRadius);
-
-    return checkIntervalOverlap(innerInvRadiusMin, innerInvRadiusMax, bridgeInvRadiusMin, bridgeInvRadiusMax);
-  }
-
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool matchRadiiBBBEE23478(TAcc const& acc,
-                                                           float innerRadius,
-                                                           float bridgeRadius,
-                                                           float outerRadius,
-                                                           float bridgeRadiusMin2S,
-                                                           float bridgeRadiusMax2S) {
-    float innerInvRadiusMin, innerInvRadiusMax, bridgeInvRadiusMin, bridgeInvRadiusMax;
-
-    float innerInvRadiusErrorBound = 0.2097f;
-    float bridgeInvRadiusErrorBound = 0.8557f;
-
-    innerInvRadiusMax = (1.f + innerInvRadiusErrorBound) / innerRadius;
-    innerInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - innerInvRadiusErrorBound) / innerRadius);
-
-    bridgeInvRadiusMax = (1.f + bridgeInvRadiusErrorBound) / bridgeRadius;
-    bridgeInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - bridgeInvRadiusErrorBound) / bridgeRadius);
-
-    return checkIntervalOverlap(innerInvRadiusMin,
-                                innerInvRadiusMax,
-                                alpaka::math::min(acc, bridgeInvRadiusMin, 1.0f / bridgeRadiusMax2S),
-                                alpaka::math::max(acc, bridgeInvRadiusMax, 1.0f / bridgeRadiusMin2S));
-  }
-
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool matchRadiiBBBEE34578(TAcc const& acc,
-                                                           float innerRadius,
-                                                           float bridgeRadius,
-                                                           float outerRadius,
-                                                           float bridgeRadiusMin2S,
-                                                           float bridgeRadiusMax2S) {
-    float innerInvRadiusMin, innerInvRadiusMax, bridgeInvRadiusMin, bridgeInvRadiusMax;
-
-    float innerInvRadiusErrorBound = 0.066f;
-    float bridgeInvRadiusErrorBound = 0.617f;
-
-    innerInvRadiusMax = (1.f + innerInvRadiusErrorBound) / innerRadius;
-    innerInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - innerInvRadiusErrorBound) / innerRadius);
-
-    bridgeInvRadiusMax = (1.f + bridgeInvRadiusErrorBound) / bridgeRadius;
-    bridgeInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - bridgeInvRadiusErrorBound) / bridgeRadius);
-
-    return checkIntervalOverlap(innerInvRadiusMin,
-                                innerInvRadiusMax,
-                                alpaka::math::min(acc, bridgeInvRadiusMin, 1.0f / bridgeRadiusMax2S),
-                                alpaka::math::max(acc, bridgeInvRadiusMax, 1.0f / bridgeRadiusMin2S));
-  }
-
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool matchRadiiBBEEE(TAcc const& acc,
-                                                      float innerRadius,
-                                                      float bridgeRadius,
-                                                      float outerRadius,
-                                                      float bridgeRadiusMin2S,
-                                                      float bridgeRadiusMax2S) {
-    float innerInvRadiusMin, innerInvRadiusMax, bridgeInvRadiusMin, bridgeInvRadiusMax;
-
-    float innerInvRadiusErrorBound = 0.6376f;
-    float bridgeInvRadiusErrorBound = 2.1381f;
-
-    if (innerRadius * k2Rinv1GeVf > 1.f)  //as good as no selections!
-    {
-      innerInvRadiusErrorBound = 12.9173f;
-      bridgeInvRadiusErrorBound = 5.1700f;
-    }
-
-    innerInvRadiusMax = (1.f + innerInvRadiusErrorBound) / innerRadius;
-    innerInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - innerInvRadiusErrorBound) / innerRadius);
-
-    bridgeInvRadiusMax = (1.f + bridgeInvRadiusErrorBound) / bridgeRadius;
-    bridgeInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - bridgeInvRadiusErrorBound) / bridgeRadius);
-
-    return checkIntervalOverlap(innerInvRadiusMin,
-                                innerInvRadiusMax,
-                                alpaka::math::min(acc, bridgeInvRadiusMin, 1.0f / bridgeRadiusMax2S),
-                                alpaka::math::max(acc, bridgeInvRadiusMax, 1.0f / bridgeRadiusMin2S));
-  }
-
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool matchRadiiBEEEE(TAcc const& acc,
-                                                      float innerRadius,
-                                                      float bridgeRadius,
-                                                      float outerRadius,
-                                                      float innerRadiusMin2S,
-                                                      float innerRadiusMax2S,
-                                                      float bridgeRadiusMin2S,
-                                                      float bridgeRadiusMax2S) {
-    float innerInvRadiusMin, innerInvRadiusMax, bridgeInvRadiusMin, bridgeInvRadiusMax;
-
-    float innerInvRadiusErrorBound = 1.9382f;
-    float bridgeInvRadiusErrorBound = 3.7280f;
-
-    if (innerRadius * k2Rinv1GeVf > 1.f) {
-      innerInvRadiusErrorBound = 23.2713f;
-      bridgeInvRadiusErrorBound = 21.7980f;
-    }
-
-    innerInvRadiusMax = (1.f + innerInvRadiusErrorBound) / innerRadius;
-    innerInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - innerInvRadiusErrorBound) / innerRadius);
-
-    bridgeInvRadiusMax = (1.f + bridgeInvRadiusErrorBound) / bridgeRadius;
-    bridgeInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - bridgeInvRadiusErrorBound) / bridgeRadius);
-
-    return checkIntervalOverlap(alpaka::math::min(acc, innerInvRadiusMin, 1.0f / innerRadiusMax2S),
-                                alpaka::math::max(acc, innerInvRadiusMax, 1.0f / innerRadiusMin2S),
-                                alpaka::math::min(acc, bridgeInvRadiusMin, 1.0f / bridgeRadiusMax2S),
-                                alpaka::math::max(acc, bridgeInvRadiusMax, 1.0f / bridgeRadiusMin2S));
-  }
-
-  template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool matchRadiiEEEEE(TAcc const& acc,
-                                                      float innerRadius,
-                                                      float bridgeRadius,
-                                                      float outerRadius,
-                                                      float innerRadiusMin2S,
-                                                      float innerRadiusMax2S,
-                                                      float bridgeRadiusMin2S,
-                                                      float bridgeRadiusMax2S) {
-    float innerInvRadiusMin, innerInvRadiusMax, bridgeInvRadiusMin, bridgeInvRadiusMax;
-
-    float innerInvRadiusErrorBound = 1.9382f;
-    float bridgeInvRadiusErrorBound = 2.2091f;
-
-    if (innerRadius * k2Rinv1GeVf > 1.f) {
-      innerInvRadiusErrorBound = 22.5226f;
-      bridgeInvRadiusErrorBound = 21.0966f;
-    }
-
-    innerInvRadiusMax = (1.f + innerInvRadiusErrorBound) / innerRadius;
-    innerInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - innerInvRadiusErrorBound) / innerRadius);
-
-    bridgeInvRadiusMax = (1.f + bridgeInvRadiusErrorBound) / bridgeRadius;
-    bridgeInvRadiusMin = alpaka::math::max(acc, 0.f, (1.f - bridgeInvRadiusErrorBound) / bridgeRadius);
-
-    return checkIntervalOverlap(alpaka::math::min(acc, innerInvRadiusMin, 1.0f / innerRadiusMax2S),
-                                alpaka::math::max(acc, innerInvRadiusMax, 1.0f / innerRadiusMin2S),
-                                alpaka::math::min(acc, bridgeInvRadiusMin, 1.0f / bridgeRadiusMax2S),
-                                alpaka::math::max(acc, bridgeInvRadiusMax, 1.0f / bridgeRadiusMin2S));
-  }
-
   template <typename TAcc>
   ALPAKA_FN_ACC ALPAKA_FN_INLINE void computeSigmasForRegression(TAcc const& acc,
                                                                  ModulesConst modules,
@@ -1099,15 +769,15 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
   }
 
   template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE void runDeltaBetaIterationsT5(TAcc const& acc,
-                                                               float& betaIn,
-                                                               float& betaOut,
-                                                               float betaAv,
-                                                               float& pt_beta,
-                                                               float sdIn_dr,
-                                                               float sdOut_dr,
-                                                               float dr,
-                                                               float lIn) {
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE void runDeltaBetaIterations(TAcc const& acc,
+                                                             float& betaIn,
+                                                             float& betaOut,
+                                                             float betaAv,
+                                                             float& pt_beta,
+                                                             float sdIn_dr,
+                                                             float sdOut_dr,
+                                                             float dr,
+                                                             float lIn) {
     if (lIn == 0) {
       betaOut += alpaka::math::copysign(
           acc,
@@ -1198,83 +868,33 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
   }
 
   template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runQuintupletDefaultAlgoBBBB(TAcc const& acc,
-                                                                   ModulesConst modules,
-                                                                   MiniDoubletsConst mds,
-                                                                   SegmentsConst segments,
-                                                                   uint16_t innerInnerLowerModuleIndex,
-                                                                   uint16_t innerOuterLowerModuleIndex,
-                                                                   uint16_t outerInnerLowerModuleIndex,
-                                                                   uint16_t outerOuterLowerModuleIndex,
-                                                                   unsigned int innerSegmentIndex,
-                                                                   unsigned int outerSegmentIndex,
-                                                                   unsigned int firstMDIndex,
-                                                                   unsigned int secondMDIndex,
-                                                                   unsigned int thirdMDIndex,
-                                                                   unsigned int fourthMDIndex) {
-    bool isPS_InLo = (modules.moduleType()[innerInnerLowerModuleIndex] == PS);
-    bool isPS_OutLo = (modules.moduleType()[outerInnerLowerModuleIndex] == PS);
-
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runQuintupletdBetaCutBBBB(TAcc const& acc,
+                                                                ModulesConst modules,
+                                                                MiniDoubletsConst mds,
+                                                                SegmentsConst segments,
+                                                                uint16_t innerInnerLowerModuleIndex,
+                                                                uint16_t innerOuterLowerModuleIndex,
+                                                                uint16_t outerInnerLowerModuleIndex,
+                                                                uint16_t outerOuterLowerModuleIndex,
+                                                                unsigned int innerSegmentIndex,
+                                                                unsigned int outerSegmentIndex,
+                                                                unsigned int firstMDIndex,
+                                                                unsigned int secondMDIndex,
+                                                                unsigned int thirdMDIndex,
+                                                                unsigned int fourthMDIndex,
+                                                                float& dBeta,
+                                                                const float ptCut) {
     float rt_InLo = mds.anchorRt()[firstMDIndex];
     float rt_InOut = mds.anchorRt()[secondMDIndex];
     float rt_OutLo = mds.anchorRt()[thirdMDIndex];
 
     float z_InLo = mds.anchorZ()[firstMDIndex];
-    float z_InOut = mds.anchorZ()[secondMDIndex];
     float z_OutLo = mds.anchorZ()[thirdMDIndex];
 
-    float alpha1GeV_OutLo =
-        alpaka::math::asin(acc, alpaka::math::min(acc, rt_OutLo * k2Rinv1GeVf / ptCut, kSinAlphaMax));
-
-    float rtRatio_OutLoInLo = rt_OutLo / rt_InLo;  // Outer segment beginning rt divided by inner segment beginning rt;
-    float dzDrtScale =
-        alpaka::math::tan(acc, alpha1GeV_OutLo) / alpha1GeV_OutLo;  // The track can bend in r-z plane slightly
-    float zpitch_InLo = (isPS_InLo ? kPixelPSZpitch : kStrip2SZpitch);
-    float zpitch_OutLo = (isPS_OutLo ? kPixelPSZpitch : kStrip2SZpitch);
-
-    float zHi = z_InLo + (z_InLo + kDeltaZLum) * (rtRatio_OutLoInLo - 1.f) * (z_InLo < 0.f ? 1.f : dzDrtScale) +
-                (zpitch_InLo + zpitch_OutLo);
-    float zLo = z_InLo + (z_InLo - kDeltaZLum) * (rtRatio_OutLoInLo - 1.f) * (z_InLo > 0.f ? 1.f : dzDrtScale) -
-                (zpitch_InLo + zpitch_OutLo);
-
-    //Cut 1 - z compatibility
-    if ((z_OutLo < zLo) || (z_OutLo > zHi))
-      return false;
-
-    float drt_OutLo_InLo = (rt_OutLo - rt_InLo);
     float r3_InLo = alpaka::math::sqrt(acc, z_InLo * z_InLo + rt_InLo * rt_InLo);
     float drt_InSeg = rt_InOut - rt_InLo;
-    float dz_InSeg = z_InOut - z_InLo;
-    float dr3_InSeg = alpaka::math::sqrt(acc, rt_InOut * rt_InOut + z_InOut * z_InOut) -
-                      alpaka::math::sqrt(acc, rt_InLo * rt_InLo + z_InLo * z_InLo);
-
-    float coshEta = dr3_InSeg / drt_InSeg;
-    float dzErr = (zpitch_InLo + zpitch_OutLo) * (zpitch_InLo + zpitch_OutLo) * 2.f;
 
     float thetaMuls2 = (kMulsInGeV * kMulsInGeV) * (0.1f + 0.2f * (rt_OutLo - rt_InLo) / 50.f) * (r3_InLo / rt_InLo);
-    float muls2 = thetaMuls2 * 9.f / (ptCut * ptCut) * 16.f;
-    dzErr += muls2 * drt_OutLo_InLo * drt_OutLo_InLo / 3.f * coshEta * coshEta;
-    dzErr = alpaka::math::sqrt(acc, dzErr);
-
-    // Constructing upper and lower bound
-    const float dzMean = dz_InSeg / drt_InSeg * drt_OutLo_InLo;
-    const float zWindow =
-        dzErr / drt_InSeg * drt_OutLo_InLo +
-        (zpitch_InLo + zpitch_OutLo);  //FIXME for ptCut lower than ~0.8 need to add curv path correction
-    float zLoPointed = z_InLo + dzMean * (z_InLo > 0.f ? 1.f : dzDrtScale) - zWindow;
-    float zHiPointed = z_InLo + dzMean * (z_InLo < 0.f ? 1.f : dzDrtScale) + zWindow;
-
-    // Cut #2: Pointed Z (Inner segment two MD points to outer segment inner MD)
-    if ((z_OutLo < zLoPointed) || (z_OutLo > zHiPointed))
-      return false;
-
-    float pvOffset = 0.1f / rt_OutLo;
-    float dPhiCut = alpha1GeV_OutLo + alpaka::math::sqrt(acc, muls2 + pvOffset * pvOffset);
-
-    float deltaPhiPos = phi_mpi_pi(acc, mds.anchorPhi()[fourthMDIndex] - mds.anchorPhi()[secondMDIndex]);
-    // Cut #3: FIXME:deltaPhiPos can be tighter
-    if (alpaka::math::abs(acc, deltaPhiPos) > dPhiCut)
-      return false;
 
     float midPointX = 0.5f * (mds.anchorX()[firstMDIndex] + mds.anchorX()[thirdMDIndex]);
     float midPointY = 0.5f * (mds.anchorY()[firstMDIndex] + mds.anchorY()[thirdMDIndex]);
@@ -1283,10 +903,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
 
     float dPhi = deltaPhi(acc, midPointX, midPointY, diffX, diffY);
 
-    // Cut #4: deltaPhiChange
-    if (alpaka::math::abs(acc, dPhi) > dPhiCut)
-      return false;
-
     // First obtaining the raw betaIn and betaOut values without any correction and just purely based on the mini-doublet hit positions
     float alpha_InLo = __H2F(segments.dPhiChanges()[innerSegmentIndex]);
     float alpha_OutLo = __H2F(segments.dPhiChanges()[outerSegmentIndex]);
@@ -1349,21 +965,12 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     //beta computation
     float drt_tl_axis = alpaka::math::sqrt(acc, tl_axis_x * tl_axis_x + tl_axis_y * tl_axis_y);
 
-    float corrF = 1.f;
     //innerOuterAnchor - innerInnerAnchor
     const float rt_InSeg = alpaka::math::sqrt(acc,
                                               (mds.anchorX()[secondMDIndex] - mds.anchorX()[firstMDIndex]) *
                                                       (mds.anchorX()[secondMDIndex] - mds.anchorX()[firstMDIndex]) +
                                                   (mds.anchorY()[secondMDIndex] - mds.anchorY()[firstMDIndex]) *
                                                       (mds.anchorY()[secondMDIndex] - mds.anchorY()[firstMDIndex]));
-    float betaInCut =
-        alpaka::math::asin(
-            acc, alpaka::math::min(acc, (-rt_InSeg * corrF + drt_tl_axis) * k2Rinv1GeVf / ptCut, kSinAlphaMax)) +
-        (0.02f / drt_InSeg);
-
-    //Cut #5: first beta cut
-    if (alpaka::math::abs(acc, betaInRHmin) >= betaInCut)
-      return false;
 
     float betaAv = 0.5f * (betaIn + betaOut);
     float pt_beta = drt_tl_axis * k2Rinv1GeVf / alpaka::math::sin(acc, betaAv);
@@ -1376,7 +983,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                 (mds.anchorY()[fourthMDIndex] - mds.anchorY()[thirdMDIndex]));
     float sdOut_d = mds.anchorRt()[fourthMDIndex] - mds.anchorRt()[thirdMDIndex];
 
-    runDeltaBetaIterationsT5(acc, betaIn, betaOut, betaAv, pt_beta, rt_InSeg, sdOut_dr, drt_tl_axis, lIn);
+    runDeltaBetaIterations(acc, betaIn, betaOut, betaAv, pt_beta, rt_InSeg, sdOut_dr, drt_tl_axis, lIn);
 
     const float betaInMMSF = (alpaka::math::abs(acc, betaInRHmin + betaInRHmax) > 0)
                                  ? (2.f * betaIn / alpaka::math::abs(acc, betaInRHmin + betaInRHmax))
@@ -1406,7 +1013,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     const float dBetaLum2 = (dBetaInLum + dBetaOutLum) * (dBetaInLum + dBetaOutLum);
     const float sinDPhi = alpaka::math::sin(acc, dPhi);
 
-    const float dBetaRIn2 = 0;  // TODO-RH
     float dBetaROut = 0;
     if (isEC_lastLayer) {
       dBetaROut = (alpaka::math::sqrt(acc,
@@ -1420,116 +1026,43 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
 
     const float dBetaROut2 = dBetaROut * dBetaROut;
 
-    float betaOutCut =
-        alpaka::math::asin(acc, alpaka::math::min(acc, drt_tl_axis * k2Rinv1GeVf / ptCut, kSinAlphaMax)) +
-        (0.02f / sdOut_d) + alpaka::math::sqrt(acc, dBetaLum2 + dBetaMuls2);
-
-    //Cut #6: The real beta cut
-    if (alpaka::math::abs(acc, betaOut) >= betaOutCut)
-      return false;
-
     float dBetaRes = 0.02f / alpaka::math::min(acc, sdOut_d, drt_InSeg);
     float dBetaCut2 =
-        (dBetaRes * dBetaRes * 2.0f + dBetaMuls2 + dBetaLum2 + dBetaRIn2 + dBetaROut2 +
+        (dBetaRes * dBetaRes * 2.0f + dBetaMuls2 + dBetaLum2 + dBetaROut2 +
          0.25f *
              (alpaka::math::abs(acc, betaInRHmin - betaInRHmax) + alpaka::math::abs(acc, betaOutRHmin - betaOutRHmax)) *
              (alpaka::math::abs(acc, betaInRHmin - betaInRHmax) + alpaka::math::abs(acc, betaOutRHmin - betaOutRHmax)));
 
-    float dBeta = betaIn - betaOut;
+    dBeta = betaIn - betaOut;
     return dBeta * dBeta <= dBetaCut2;
   }
 
   template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runQuintupletDefaultAlgoBBEE(TAcc const& acc,
-                                                                   ModulesConst modules,
-                                                                   MiniDoubletsConst mds,
-                                                                   SegmentsConst segments,
-                                                                   uint16_t innerInnerLowerModuleIndex,
-                                                                   uint16_t innerOuterLowerModuleIndex,
-                                                                   uint16_t outerInnerLowerModuleIndex,
-                                                                   uint16_t outerOuterLowerModuleIndex,
-                                                                   unsigned int innerSegmentIndex,
-                                                                   unsigned int outerSegmentIndex,
-                                                                   unsigned int firstMDIndex,
-                                                                   unsigned int secondMDIndex,
-                                                                   unsigned int thirdMDIndex,
-                                                                   unsigned int fourthMDIndex) {
-    bool isPS_InLo = (modules.moduleType()[innerInnerLowerModuleIndex] == PS);
-    bool isPS_OutLo = (modules.moduleType()[outerInnerLowerModuleIndex] == PS);
-
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runQuintupletdBetaCutBBEE(TAcc const& acc,
+                                                                ModulesConst modules,
+                                                                MiniDoubletsConst mds,
+                                                                SegmentsConst segments,
+                                                                uint16_t innerInnerLowerModuleIndex,
+                                                                uint16_t innerOuterLowerModuleIndex,
+                                                                uint16_t outerInnerLowerModuleIndex,
+                                                                uint16_t outerOuterLowerModuleIndex,
+                                                                unsigned int innerSegmentIndex,
+                                                                unsigned int outerSegmentIndex,
+                                                                unsigned int firstMDIndex,
+                                                                unsigned int secondMDIndex,
+                                                                unsigned int thirdMDIndex,
+                                                                unsigned int fourthMDIndex,
+                                                                float& dBeta,
+                                                                const float ptCut) {
     float rt_InLo = mds.anchorRt()[firstMDIndex];
     float rt_InOut = mds.anchorRt()[secondMDIndex];
     float rt_OutLo = mds.anchorRt()[thirdMDIndex];
 
     float z_InLo = mds.anchorZ()[firstMDIndex];
-    float z_InOut = mds.anchorZ()[secondMDIndex];
     float z_OutLo = mds.anchorZ()[thirdMDIndex];
 
-    float alpha1GeV_OutLo =
-        alpaka::math::asin(acc, alpaka::math::min(acc, rt_OutLo * k2Rinv1GeVf / ptCut, kSinAlphaMax));
-
-    float dzDrtScale =
-        alpaka::math::tan(acc, alpha1GeV_OutLo) / alpha1GeV_OutLo;  // The track can bend in r-z plane slightly
-    float zpitch_InLo = (isPS_InLo ? kPixelPSZpitch : kStrip2SZpitch);
-    float zpitch_OutLo = (isPS_OutLo ? kPixelPSZpitch : kStrip2SZpitch);
-    float zGeom = zpitch_InLo + zpitch_OutLo;
-
-    // Cut #0: Preliminary (Only here in endcap case)
-    if (z_InLo * z_OutLo <= 0)
-      return false;
-
-    float dLum = alpaka::math::copysign(acc, kDeltaZLum, z_InLo);
-    bool isOutSgInnerMDPS = modules.moduleType()[outerInnerLowerModuleIndex] == PS;
-    float rtGeom1 = isOutSgInnerMDPS ? kPixelPSZpitch : kStrip2SZpitch;
-    float zGeom1 = alpaka::math::copysign(acc, zGeom, z_InLo);
-    float rtLo = rt_InLo * (1.f + (z_OutLo - z_InLo - zGeom1) / (z_InLo + zGeom1 + dLum) / dzDrtScale) -
-                 rtGeom1;  //slope correction only on the lower end
-    float rtOut = rt_OutLo;
-
-    //Cut #1: rt condition
-    if (rtOut < rtLo)
-      return false;
-
-    float zInForHi = z_InLo - zGeom1 - dLum;
-    if (zInForHi * z_InLo < 0) {
-      zInForHi = alpaka::math::copysign(acc, 0.1f, z_InLo);
-    }
-    float rtHi = rt_InLo * (1.f + (z_OutLo - z_InLo + zGeom1) / zInForHi) + rtGeom1;
-
-    //Cut #2: rt condition
-    if ((rt_OutLo < rtLo) || (rt_OutLo > rtHi))
-      return false;
-
     float rIn = alpaka::math::sqrt(acc, z_InLo * z_InLo + rt_InLo * rt_InLo);
-    const float drtSDIn = rt_InOut - rt_InLo;
-    const float dzSDIn = z_InOut - z_InLo;
-    const float dr3SDIn = alpaka::math::sqrt(acc, rt_InOut * rt_InOut + z_InOut * z_InOut) -
-                          alpaka::math::sqrt(acc, rt_InLo * rt_InLo + z_InLo * z_InLo);
-
-    const float coshEta = dr3SDIn / drtSDIn;  //direction estimate
-    const float dzOutInAbs = alpaka::math::abs(acc, z_OutLo - z_InLo);
-    const float multDzDr = dzOutInAbs * coshEta / (coshEta * coshEta - 1.f);
-    const float zGeom1_another = kPixelPSZpitch;
-    float kZ = (z_OutLo - z_InLo) / dzSDIn;
-    float drtErr =
-        zGeom1_another * zGeom1_another * drtSDIn * drtSDIn / dzSDIn / dzSDIn * (1.f - 2.f * kZ + 2.f * kZ * kZ);
     const float thetaMuls2 = (kMulsInGeV * kMulsInGeV) * (0.1f + 0.2f * (rt_OutLo - rt_InLo) / 50.f) * (rIn / rt_InLo);
-    const float muls2 = thetaMuls2 * 9.f / (ptCut * ptCut) * 16.f;
-    drtErr += muls2 * multDzDr * multDzDr / 3.f * coshEta * coshEta;
-    drtErr = alpaka::math::sqrt(acc, drtErr);
-
-    //Cut #3: rt-z pointed
-    if ((kZ < 0) || (rtOut < rtLo) || (rtOut > rtHi))
-      return false;
-
-    const float pvOffset = 0.1f / rt_OutLo;
-    float dPhiCut = alpha1GeV_OutLo + alpaka::math::sqrt(acc, muls2 + pvOffset * pvOffset);
-
-    float deltaPhiPos = phi_mpi_pi(acc, mds.anchorPhi()[fourthMDIndex] - mds.anchorPhi()[secondMDIndex]);
-
-    //Cut #4: deltaPhiPos can be tighter
-    if (alpaka::math::abs(acc, deltaPhiPos) > dPhiCut)
-      return false;
 
     float midPointX = 0.5f * (mds.anchorX()[firstMDIndex] + mds.anchorX()[thirdMDIndex]);
     float midPointY = 0.5f * (mds.anchorY()[firstMDIndex] + mds.anchorY()[thirdMDIndex]);
@@ -1537,25 +1070,21 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     float diffY = mds.anchorY()[thirdMDIndex] - mds.anchorY()[firstMDIndex];
 
     float dPhi = deltaPhi(acc, midPointX, midPointY, diffX, diffY);
-    // Cut #5: deltaPhiChange
-    if (alpaka::math::abs(acc, dPhi) > dPhiCut)
-      return false;
 
     float sdIn_alpha = __H2F(segments.dPhiChanges()[innerSegmentIndex]);
     float sdIn_alpha_min = __H2F(segments.dPhiChangeMins()[innerSegmentIndex]);
     float sdIn_alpha_max = __H2F(segments.dPhiChangeMaxs()[innerSegmentIndex]);
     float sdOut_alpha = sdIn_alpha;
 
-    float sdOut_alphaOut = phi_mpi_pi(acc,
-                                      phi(acc,
-                                          mds.anchorX()[fourthMDIndex] - mds.anchorX()[thirdMDIndex],
-                                          mds.anchorY()[fourthMDIndex] - mds.anchorY()[thirdMDIndex]) -
-                                          mds.anchorPhi()[fourthMDIndex]);
+    float sdOut_dPhiPos = phi_mpi_pi(acc, mds.anchorPhi()[fourthMDIndex] - mds.anchorPhi()[thirdMDIndex]);
+
+    float sdOut_dPhiChange = __H2F(segments.dPhiChanges()[outerSegmentIndex]);
+    float sdOut_dPhiChange_min = __H2F(segments.dPhiChangeMins()[outerSegmentIndex]);
+    float sdOut_dPhiChange_max = __H2F(segments.dPhiChangeMaxs()[outerSegmentIndex]);
 
-    float sdOut_alphaOut_min = phi_mpi_pi(
-        acc, __H2F(segments.dPhiChangeMins()[outerSegmentIndex]) - __H2F(segments.dPhiMins()[outerSegmentIndex]));
-    float sdOut_alphaOut_max = phi_mpi_pi(
-        acc, __H2F(segments.dPhiChangeMaxs()[outerSegmentIndex]) - __H2F(segments.dPhiMaxs()[outerSegmentIndex]));
+    float sdOut_alphaOutRHmin = phi_mpi_pi(acc, sdOut_dPhiChange_min - sdOut_dPhiPos);
+    float sdOut_alphaOutRHmax = phi_mpi_pi(acc, sdOut_dPhiChange_max - sdOut_dPhiPos);
+    float sdOut_alphaOut = phi_mpi_pi(acc, sdOut_dPhiChange - sdOut_dPhiPos);
 
     float tl_axis_x = mds.anchorX()[fourthMDIndex] - mds.anchorX()[firstMDIndex];
     float tl_axis_y = mds.anchorY()[fourthMDIndex] - mds.anchorY()[firstMDIndex];
@@ -1577,8 +1106,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
       betaInRHmax = betaIn - sdIn_alpha_max + sdIn_alpha;
     }
 
-    betaOutRHmin = betaOut - sdOut_alphaOut_min + sdOut_alphaOut;
-    betaOutRHmax = betaOut - sdOut_alphaOut_max + sdOut_alphaOut;
+    betaOutRHmin = betaOut - sdOut_alphaOutRHmin + sdOut_alphaOut;
+    betaOutRHmax = betaOut - sdOut_alphaOutRHmax + sdOut_alphaOut;
 
     float swapTemp;
     if (alpaka::math::abs(acc, betaOutRHmin) > alpaka::math::abs(acc, betaOutRHmax)) {
@@ -1601,14 +1130,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     float sdIn_d = rt_InOut - rt_InLo;
 
     float dr = alpaka::math::sqrt(acc, tl_axis_x * tl_axis_x + tl_axis_y * tl_axis_y);
-    const float corrF = 1.f;
-    float betaInCut =
-        alpaka::math::asin(acc, alpaka::math::min(acc, (-sdIn_dr * corrF + dr) * k2Rinv1GeVf / ptCut, kSinAlphaMax)) +
-        (0.02f / sdIn_d);
-
-    //Cut #6: first beta cut
-    if (alpaka::math::abs(acc, betaInRHmin) >= betaInCut)
-      return false;
 
     float betaAv = 0.5f * (betaIn + betaOut);
     float pt_beta = dr * k2Rinv1GeVf / alpaka::math::sin(acc, betaAv);
@@ -1623,7 +1144,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                 (mds.anchorY()[fourthMDIndex] - mds.anchorY()[thirdMDIndex]));
     float sdOut_d = mds.anchorRt()[fourthMDIndex] - mds.anchorRt()[thirdMDIndex];
 
-    runDeltaBetaIterationsT5(acc, betaIn, betaOut, betaAv, pt_beta, sdIn_dr, sdOut_dr, dr, lIn);
+    runDeltaBetaIterations(acc, betaIn, betaOut, betaAv, pt_beta, sdIn_dr, sdOut_dr, dr, lIn);
 
     const float betaInMMSF = (alpaka::math::abs(acc, betaInRHmin + betaInRHmax) > 0)
                                  ? (2.f * betaIn / alpaka::math::abs(acc, betaInRHmin + betaInRHmax))
@@ -1666,12 +1187,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     }
 
     const float dBetaROut2 = dBetaROut * dBetaROut;
-    float betaOutCut = alpaka::math::asin(acc, alpaka::math::min(acc, dr * k2Rinv1GeVf / ptCut, kSinAlphaMax)) +
-                       (0.02f / sdOut_d) + alpaka::math::sqrt(acc, dBetaLum2 + dBetaMuls2);
-
-    //Cut #6: The real beta cut
-    if (alpaka::math::abs(acc, betaOut) >= betaOutCut)
-      return false;
 
     float dBetaRes = 0.02f / alpaka::math::min(acc, sdOut_d, sdIn_d);
     float dBetaCut2 =
@@ -1679,117 +1194,35 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
          0.25f *
              (alpaka::math::abs(acc, betaInRHmin - betaInRHmax) + alpaka::math::abs(acc, betaOutRHmin - betaOutRHmax)) *
              (alpaka::math::abs(acc, betaInRHmin - betaInRHmax) + alpaka::math::abs(acc, betaOutRHmin - betaOutRHmax)));
-    float dBeta = betaIn - betaOut;
-    //Cut #7: Cut on dBet
+    dBeta = betaIn - betaOut;
     return dBeta * dBeta <= dBetaCut2;
   }
 
   template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runQuintupletDefaultAlgoEEEE(TAcc const& acc,
-                                                                   ModulesConst modules,
-                                                                   MiniDoubletsConst mds,
-                                                                   SegmentsConst segments,
-                                                                   uint16_t innerInnerLowerModuleIndex,
-                                                                   uint16_t innerOuterLowerModuleIndex,
-                                                                   uint16_t outerInnerLowerModuleIndex,
-                                                                   uint16_t outerOuterLowerModuleIndex,
-                                                                   unsigned int innerSegmentIndex,
-                                                                   unsigned int outerSegmentIndex,
-                                                                   unsigned int firstMDIndex,
-                                                                   unsigned int secondMDIndex,
-                                                                   unsigned int thirdMDIndex,
-                                                                   unsigned int fourthMDIndex) {
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runQuintupletdBetaCutEEEE(TAcc const& acc,
+                                                                ModulesConst modules,
+                                                                MiniDoubletsConst mds,
+                                                                SegmentsConst segments,
+                                                                uint16_t innerInnerLowerModuleIndex,
+                                                                uint16_t innerOuterLowerModuleIndex,
+                                                                uint16_t outerInnerLowerModuleIndex,
+                                                                uint16_t outerOuterLowerModuleIndex,
+                                                                unsigned int innerSegmentIndex,
+                                                                unsigned int outerSegmentIndex,
+                                                                unsigned int firstMDIndex,
+                                                                unsigned int secondMDIndex,
+                                                                unsigned int thirdMDIndex,
+                                                                unsigned int fourthMDIndex,
+                                                                float& dBeta,
+                                                                const float ptCut) {
     float rt_InLo = mds.anchorRt()[firstMDIndex];
     float rt_InOut = mds.anchorRt()[secondMDIndex];
     float rt_OutLo = mds.anchorRt()[thirdMDIndex];
 
     float z_InLo = mds.anchorZ()[firstMDIndex];
-    float z_InOut = mds.anchorZ()[secondMDIndex];
     float z_OutLo = mds.anchorZ()[thirdMDIndex];
 
-    float alpha1GeV_OutLo =
-        alpaka::math::asin(acc, alpaka::math::min(acc, rt_OutLo * k2Rinv1GeVf / ptCut, kSinAlphaMax));
-
-    float dzDrtScale =
-        alpaka::math::tan(acc, alpha1GeV_OutLo) / alpha1GeV_OutLo;  // The track can bend in r-z plane slightly
-
-    // Cut #0: Preliminary (Only here in endcap case)
-    if ((z_InLo * z_OutLo) <= 0)
-      return false;
-
-    float dLum = alpaka::math::copysign(acc, kDeltaZLum, z_InLo);
-    bool isOutSgInnerMDPS = modules.moduleType()[outerInnerLowerModuleIndex] == PS;
-    bool isInSgInnerMDPS = modules.moduleType()[innerInnerLowerModuleIndex] == PS;
-
-    float rtGeom = (isInSgInnerMDPS and isOutSgInnerMDPS)  ? 2.f * kPixelPSZpitch
-                   : (isInSgInnerMDPS or isOutSgInnerMDPS) ? kPixelPSZpitch + kStrip2SZpitch
-                                                           : 2.f * kStrip2SZpitch;
-
-    float dz = z_OutLo - z_InLo;
-    float rtLo = rt_InLo * (1.f + dz / (z_InLo + dLum) / dzDrtScale) - rtGeom;  //slope correction only on the lower end
-
-    float rtOut = rt_OutLo;
-
-    //Cut #1: rt condition
-
-    float rtHi = rt_InLo * (1.f + dz / (z_InLo - dLum)) + rtGeom;
-
-    if ((rtOut < rtLo) || (rtOut > rtHi))
-      return false;
-
-    bool isInSgOuterMDPS = modules.moduleType()[innerOuterLowerModuleIndex] == PS;
-
-    const float drtSDIn = rt_InOut - rt_InLo;
-    const float dzSDIn = z_InOut - z_InLo;
-    const float dr3SDIn = alpaka::math::sqrt(acc, rt_InOut * rt_InOut + z_InOut * z_InOut) -
-                          alpaka::math::sqrt(acc, rt_InLo * rt_InLo + z_InLo * z_InLo);
-    float coshEta = dr3SDIn / drtSDIn;  //direction estimate
-    float dzOutInAbs = alpaka::math::abs(acc, z_OutLo - z_InLo);
-    float multDzDr = dzOutInAbs * coshEta / (coshEta * coshEta - 1.f);
-
-    float kZ = (z_OutLo - z_InLo) / dzSDIn;
     float thetaMuls2 = (kMulsInGeV * kMulsInGeV) * (0.1f + 0.2f * (rt_OutLo - rt_InLo) / 50.f);
-
-    float muls2 = thetaMuls2 * 9.f / (ptCut * ptCut) * 16.f;
-
-    float drtErr =
-        alpaka::math::sqrt(acc,
-                           kPixelPSZpitch * kPixelPSZpitch * 2.f / (dzSDIn * dzSDIn) * (dzOutInAbs * dzOutInAbs) +
-                               muls2 * multDzDr * multDzDr / 3.f * coshEta * coshEta);
-
-    float drtMean = drtSDIn * dzOutInAbs / alpaka::math::abs(acc, dzSDIn);
-    float rtWindow = drtErr + rtGeom;
-    float rtLo_point = rt_InLo + drtMean / dzDrtScale - rtWindow;
-    float rtHi_point = rt_InLo + drtMean + rtWindow;
-
-    // Cut #3: rt-z pointed
-    // https://github.com/slava77/cms-tkph2-ntuple/blob/superDoubletLinked-91X-noMock/doubletAnalysis.C#L3765
-
-    if (isInSgInnerMDPS and isInSgOuterMDPS)  // If both PS then we can point
-    {
-      if (kZ < 0 || rtOut < rtLo_point || rtOut > rtHi_point)
-        return false;
-    }
-
-    float pvOffset = 0.1f / rtOut;
-    float dPhiCut = alpha1GeV_OutLo + alpaka::math::sqrt(acc, muls2 + pvOffset * pvOffset);
-
-    float deltaPhiPos = phi_mpi_pi(acc, mds.anchorPhi()[fourthMDIndex] - mds.anchorPhi()[secondMDIndex]);
-
-    if (alpaka::math::abs(acc, deltaPhiPos) > dPhiCut)
-      return false;
-
-    float midPointX = 0.5f * (mds.anchorX()[firstMDIndex] + mds.anchorX()[thirdMDIndex]);
-    float midPointY = 0.5f * (mds.anchorY()[firstMDIndex] + mds.anchorY()[thirdMDIndex]);
-    float diffX = mds.anchorX()[thirdMDIndex] - mds.anchorX()[firstMDIndex];
-    float diffY = mds.anchorY()[thirdMDIndex] - mds.anchorY()[firstMDIndex];
-
-    float dPhi = deltaPhi(acc, midPointX, midPointY, diffX, diffY);
-
-    // Cut #5: deltaPhiChange
-    if (alpaka::math::abs(acc, dPhi) > dPhiCut)
-      return false;
-
     float sdIn_alpha = __H2F(segments.dPhiChanges()[innerSegmentIndex]);
     float sdOut_alpha = sdIn_alpha;  //weird
     float sdOut_dPhiPos = phi_mpi_pi(acc, mds.anchorPhi()[fourthMDIndex] - mds.anchorPhi()[thirdMDIndex]);
@@ -1837,14 +1270,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     float sdIn_d = rt_InOut - rt_InLo;
 
     float dr = alpaka::math::sqrt(acc, tl_axis_x * tl_axis_x + tl_axis_y * tl_axis_y);
-    const float corrF = 1.f;
-    float betaInCut =
-        alpaka::math::asin(acc, alpaka::math::min(acc, (-sdIn_dr * corrF + dr) * k2Rinv1GeVf / ptCut, kSinAlphaMax)) +
-        (0.02f / sdIn_d);
-
-    //Cut #6: first beta cut
-    if (alpaka::math::abs(acc, betaInRHmin) >= betaInCut)
-      return false;
 
     float betaAv = 0.5f * (betaIn + betaOut);
     float pt_beta = dr * k2Rinv1GeVf / alpaka::math::sin(acc, betaAv);
@@ -1859,7 +1284,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                 (mds.anchorY()[fourthMDIndex] - mds.anchorY()[thirdMDIndex]));
     float sdOut_d = mds.anchorRt()[fourthMDIndex] - mds.anchorRt()[thirdMDIndex];
 
-    runDeltaBetaIterationsT5(acc, betaIn, betaOut, betaAv, pt_beta, sdIn_dr, sdOut_dr, dr, lIn);
+    runDeltaBetaIterations(acc, betaIn, betaOut, betaAv, pt_beta, sdIn_dr, sdOut_dr, dr, lIn);
 
     const float betaInMMSF = (alpaka::math::abs(acc, betaInRHmin + betaInRHmax) > 0)
                                  ? (2.f * betaIn / alpaka::math::abs(acc, betaInRHmin + betaInRHmax))
@@ -1888,42 +1313,33 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     const float dBetaOutLum = lOut < 11 ? 0.0f : alpaka::math::abs(acc, alphaOutAbsReg * kDeltaZLum / z_OutLo);
     const float dBetaLum2 = (dBetaInLum + dBetaOutLum) * (dBetaInLum + dBetaOutLum);
 
-    const float dBetaRIn2 = 0;  // TODO-RH
-
-    float dBetaROut2 = 0;  //TODO-RH
-    float betaOutCut = alpaka::math::asin(acc, alpaka::math::min(acc, dr * k2Rinv1GeVf / ptCut, kSinAlphaMax)) +
-                       (0.02f / sdOut_d) + alpaka::math::sqrt(acc, dBetaLum2 + dBetaMuls2);
-
-    //Cut #6: The real beta cut
-    if (alpaka::math::abs(acc, betaOut) >= betaOutCut)
-      return false;
-
     float dBetaRes = 0.02f / alpaka::math::min(acc, sdOut_d, sdIn_d);
     float dBetaCut2 =
-        (dBetaRes * dBetaRes * 2.0f + dBetaMuls2 + dBetaLum2 + dBetaRIn2 + dBetaROut2 +
+        (dBetaRes * dBetaRes * 2.0f + dBetaMuls2 + dBetaLum2 +
          0.25f *
              (alpaka::math::abs(acc, betaInRHmin - betaInRHmax) + alpaka::math::abs(acc, betaOutRHmin - betaOutRHmax)) *
              (alpaka::math::abs(acc, betaInRHmin - betaInRHmax) + alpaka::math::abs(acc, betaOutRHmin - betaOutRHmax)));
-    float dBeta = betaIn - betaOut;
-    //Cut #7: Cut on dBeta
+    dBeta = betaIn - betaOut;
     return dBeta * dBeta <= dBetaCut2;
   }
 
   template <typename TAcc>
-  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runQuintupletAlgoSelector(TAcc const& acc,
-                                                                ModulesConst modules,
-                                                                MiniDoubletsConst mds,
-                                                                SegmentsConst segments,
-                                                                uint16_t innerInnerLowerModuleIndex,
-                                                                uint16_t innerOuterLowerModuleIndex,
-                                                                uint16_t outerInnerLowerModuleIndex,
-                                                                uint16_t outerOuterLowerModuleIndex,
-                                                                unsigned int innerSegmentIndex,
-                                                                unsigned int outerSegmentIndex,
-                                                                unsigned int firstMDIndex,
-                                                                unsigned int secondMDIndex,
-                                                                unsigned int thirdMDIndex,
-                                                                unsigned int fourthMDIndex) {
+  ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runQuintupletdBetaAlgoSelector(TAcc const& acc,
+                                                                     ModulesConst modules,
+                                                                     MiniDoubletsConst mds,
+                                                                     SegmentsConst segments,
+                                                                     uint16_t innerInnerLowerModuleIndex,
+                                                                     uint16_t innerOuterLowerModuleIndex,
+                                                                     uint16_t outerInnerLowerModuleIndex,
+                                                                     uint16_t outerOuterLowerModuleIndex,
+                                                                     unsigned int innerSegmentIndex,
+                                                                     unsigned int outerSegmentIndex,
+                                                                     unsigned int firstMDIndex,
+                                                                     unsigned int secondMDIndex,
+                                                                     unsigned int thirdMDIndex,
+                                                                     unsigned int fourthMDIndex,
+                                                                     float& dBeta,
+                                                                     const float ptCut) {
     short innerInnerLowerModuleSubdet = modules.subdets()[innerInnerLowerModuleIndex];
     short innerOuterLowerModuleSubdet = modules.subdets()[innerOuterLowerModuleIndex];
     short outerInnerLowerModuleSubdet = modules.subdets()[outerInnerLowerModuleIndex];
@@ -1931,84 +1347,94 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
 
     if (innerInnerLowerModuleSubdet == Barrel and innerOuterLowerModuleSubdet == Barrel and
         outerInnerLowerModuleSubdet == Barrel and outerOuterLowerModuleSubdet == Barrel) {
-      return runQuintupletDefaultAlgoBBBB(acc,
-                                          modules,
-                                          mds,
-                                          segments,
-                                          innerInnerLowerModuleIndex,
-                                          innerOuterLowerModuleIndex,
-                                          outerInnerLowerModuleIndex,
-                                          outerOuterLowerModuleIndex,
-                                          innerSegmentIndex,
-                                          outerSegmentIndex,
-                                          firstMDIndex,
-                                          secondMDIndex,
-                                          thirdMDIndex,
-                                          fourthMDIndex);
+      return runQuintupletdBetaCutBBBB(acc,
+                                       modules,
+                                       mds,
+                                       segments,
+                                       innerInnerLowerModuleIndex,
+                                       innerOuterLowerModuleIndex,
+                                       outerInnerLowerModuleIndex,
+                                       outerOuterLowerModuleIndex,
+                                       innerSegmentIndex,
+                                       outerSegmentIndex,
+                                       firstMDIndex,
+                                       secondMDIndex,
+                                       thirdMDIndex,
+                                       fourthMDIndex,
+                                       dBeta,
+                                       ptCut);
     } else if (innerInnerLowerModuleSubdet == Barrel and innerOuterLowerModuleSubdet == Barrel and
                outerInnerLowerModuleSubdet == Endcap and outerOuterLowerModuleSubdet == Endcap) {
-      return runQuintupletDefaultAlgoBBEE(acc,
-                                          modules,
-                                          mds,
-                                          segments,
-                                          innerInnerLowerModuleIndex,
-                                          innerOuterLowerModuleIndex,
-                                          outerInnerLowerModuleIndex,
-                                          outerOuterLowerModuleIndex,
-                                          innerSegmentIndex,
-                                          outerSegmentIndex,
-                                          firstMDIndex,
-                                          secondMDIndex,
-                                          thirdMDIndex,
-                                          fourthMDIndex);
+      return runQuintupletdBetaCutBBEE(acc,
+                                       modules,
+                                       mds,
+                                       segments,
+                                       innerInnerLowerModuleIndex,
+                                       innerOuterLowerModuleIndex,
+                                       outerInnerLowerModuleIndex,
+                                       outerOuterLowerModuleIndex,
+                                       innerSegmentIndex,
+                                       outerSegmentIndex,
+                                       firstMDIndex,
+                                       secondMDIndex,
+                                       thirdMDIndex,
+                                       fourthMDIndex,
+                                       dBeta,
+                                       ptCut);
     } else if (innerInnerLowerModuleSubdet == Barrel and innerOuterLowerModuleSubdet == Barrel and
                outerInnerLowerModuleSubdet == Barrel and outerOuterLowerModuleSubdet == Endcap) {
-      return runQuintupletDefaultAlgoBBBB(acc,
-                                          modules,
-                                          mds,
-                                          segments,
-                                          innerInnerLowerModuleIndex,
-                                          innerOuterLowerModuleIndex,
-                                          outerInnerLowerModuleIndex,
-                                          outerOuterLowerModuleIndex,
-                                          innerSegmentIndex,
-                                          outerSegmentIndex,
-                                          firstMDIndex,
-                                          secondMDIndex,
-                                          thirdMDIndex,
-                                          fourthMDIndex);
+      return runQuintupletdBetaCutBBBB(acc,
+                                       modules,
+                                       mds,
+                                       segments,
+                                       innerInnerLowerModuleIndex,
+                                       innerOuterLowerModuleIndex,
+                                       outerInnerLowerModuleIndex,
+                                       outerOuterLowerModuleIndex,
+                                       innerSegmentIndex,
+                                       outerSegmentIndex,
+                                       firstMDIndex,
+                                       secondMDIndex,
+                                       thirdMDIndex,
+                                       fourthMDIndex,
+                                       dBeta,
+                                       ptCut);
     } else if (innerInnerLowerModuleSubdet == Barrel and innerOuterLowerModuleSubdet == Endcap and
                outerInnerLowerModuleSubdet == Endcap and outerOuterLowerModuleSubdet == Endcap) {
-      return runQuintupletDefaultAlgoBBEE(acc,
-                                          modules,
-                                          mds,
-                                          segments,
-                                          innerInnerLowerModuleIndex,
-                                          innerOuterLowerModuleIndex,
-                                          outerInnerLowerModuleIndex,
-                                          outerOuterLowerModuleIndex,
-                                          innerSegmentIndex,
-                                          outerSegmentIndex,
-                                          firstMDIndex,
-                                          secondMDIndex,
-                                          thirdMDIndex,
-                                          fourthMDIndex);
+      return runQuintupletdBetaCutBBEE(acc,
+                                       modules,
+                                       mds,
+                                       segments,
+                                       innerInnerLowerModuleIndex,
+                                       innerOuterLowerModuleIndex,
+                                       outerInnerLowerModuleIndex,
+                                       outerOuterLowerModuleIndex,
+                                       innerSegmentIndex,
+                                       outerSegmentIndex,
+                                       firstMDIndex,
+                                       secondMDIndex,
+                                       thirdMDIndex,
+                                       fourthMDIndex,
+                                       dBeta,
+                                       ptCut);
     } else if (innerInnerLowerModuleSubdet == Endcap and innerOuterLowerModuleSubdet == Endcap and
                outerInnerLowerModuleSubdet == Endcap and outerOuterLowerModuleSubdet == Endcap) {
-      return runQuintupletDefaultAlgoEEEE(acc,
-                                          modules,
-                                          mds,
-                                          segments,
-                                          innerInnerLowerModuleIndex,
-                                          innerOuterLowerModuleIndex,
-                                          outerInnerLowerModuleIndex,
-                                          outerOuterLowerModuleIndex,
-                                          innerSegmentIndex,
-                                          outerSegmentIndex,
-                                          firstMDIndex,
-                                          secondMDIndex,
-                                          thirdMDIndex,
-                                          fourthMDIndex);
+      return runQuintupletdBetaCutEEEE(acc,
+                                       modules,
+                                       mds,
+                                       segments,
+                                       innerInnerLowerModuleIndex,
+                                       innerOuterLowerModuleIndex,
+                                       outerInnerLowerModuleIndex,
+                                       outerOuterLowerModuleIndex,
+                                       innerSegmentIndex,
+                                       outerSegmentIndex,
+                                       firstMDIndex,
+                                       secondMDIndex,
+                                       thirdMDIndex,
+                                       fourthMDIndex,
+                                       dBeta,
+                                       ptCut);
     }
 
     return false;
@@ -2036,7 +1462,10 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                float& rzChiSquared,
                                                                float& chiSquared,
                                                                float& nonAnchorChiSquared,
-                                                               bool& tightCutFlag) {
+                                                               float& dBeta1,
+                                                               float& dBeta2,
+                                                               bool& tightCutFlag,
+                                                               const float ptCut) {
     unsigned int firstSegmentIndex = triplets.segmentIndices()[innerTripletIndex][0];
     unsigned int secondSegmentIndex = triplets.segmentIndices()[innerTripletIndex][1];
     unsigned int thirdSegmentIndex = triplets.segmentIndices()[outerTripletIndex][0];
@@ -2057,38 +1486,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     unsigned int fourthMDIndex = segments.mdIndices()[thirdSegmentIndex][1];
     unsigned int fifthMDIndex = segments.mdIndices()[fourthSegmentIndex][1];
 
-    if (not runQuintupletAlgoSelector(acc,
-                                      modules,
-                                      mds,
-                                      segments,
-                                      lowerModuleIndex1,
-                                      lowerModuleIndex2,
-                                      lowerModuleIndex3,
-                                      lowerModuleIndex4,
-                                      firstSegmentIndex,
-                                      thirdSegmentIndex,
-                                      firstMDIndex,
-                                      secondMDIndex,
-                                      thirdMDIndex,
-                                      fourthMDIndex))
-      return false;
-
-    if (not runQuintupletAlgoSelector(acc,
-                                      modules,
-                                      mds,
-                                      segments,
-                                      lowerModuleIndex1,
-                                      lowerModuleIndex2,
-                                      lowerModuleIndex4,
-                                      lowerModuleIndex5,
-                                      firstSegmentIndex,
-                                      fourthSegmentIndex,
-                                      firstMDIndex,
-                                      secondMDIndex,
-                                      fourthMDIndex,
-                                      fifthMDIndex))
-      return false;
-
     float x1 = mds.anchorX()[firstMDIndex];
     float x2 = mds.anchorX()[secondMDIndex];
     float x3 = mds.anchorX()[thirdMDIndex];
@@ -2101,73 +1498,61 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     float y4 = mds.anchorY()[fourthMDIndex];
     float y5 = mds.anchorY()[fifthMDIndex];
 
-    //construct the arrays
-    float x1Vec[] = {x1, x1, x1};
-    float y1Vec[] = {y1, y1, y1};
-    float x2Vec[] = {x2, x2, x2};
-    float y2Vec[] = {y2, y2, y2};
-    float x3Vec[] = {x3, x3, x3};
-    float y3Vec[] = {y3, y3, y3};
-
-    if (modules.subdets()[lowerModuleIndex1] == Endcap and modules.moduleType()[lowerModuleIndex1] == TwoS) {
-      x1Vec[1] = mds.anchorLowEdgeX()[firstMDIndex];
-      x1Vec[2] = mds.anchorHighEdgeX()[firstMDIndex];
-
-      y1Vec[1] = mds.anchorLowEdgeY()[firstMDIndex];
-      y1Vec[2] = mds.anchorHighEdgeY()[firstMDIndex];
-    }
-    if (modules.subdets()[lowerModuleIndex2] == Endcap and modules.moduleType()[lowerModuleIndex2] == TwoS) {
-      x2Vec[1] = mds.anchorLowEdgeX()[secondMDIndex];
-      x2Vec[2] = mds.anchorHighEdgeX()[secondMDIndex];
-
-      y2Vec[1] = mds.anchorLowEdgeY()[secondMDIndex];
-      y2Vec[2] = mds.anchorHighEdgeY()[secondMDIndex];
-    }
-    if (modules.subdets()[lowerModuleIndex3] == Endcap and modules.moduleType()[lowerModuleIndex3] == TwoS) {
-      x3Vec[1] = mds.anchorLowEdgeX()[thirdMDIndex];
-      x3Vec[2] = mds.anchorHighEdgeX()[thirdMDIndex];
-
-      y3Vec[1] = mds.anchorLowEdgeY()[thirdMDIndex];
-      y3Vec[2] = mds.anchorHighEdgeY()[thirdMDIndex];
-    }
-
-    float innerRadiusMin2S, innerRadiusMax2S;
-    computeErrorInRadius(acc, x1Vec, y1Vec, x2Vec, y2Vec, x3Vec, y3Vec, innerRadiusMin2S, innerRadiusMax2S);
-
-    for (int i = 0; i < 3; i++) {
-      x1Vec[i] = x4;
-      y1Vec[i] = y4;
-    }
-    if (modules.subdets()[lowerModuleIndex4] == Endcap and modules.moduleType()[lowerModuleIndex4] == TwoS) {
-      x1Vec[1] = mds.anchorLowEdgeX()[fourthMDIndex];
-      x1Vec[2] = mds.anchorHighEdgeX()[fourthMDIndex];
-
-      y1Vec[1] = mds.anchorLowEdgeY()[fourthMDIndex];
-      y1Vec[2] = mds.anchorHighEdgeY()[fourthMDIndex];
-    }
-
-    float bridgeRadiusMin2S, bridgeRadiusMax2S;
-    computeErrorInRadius(acc, x2Vec, y2Vec, x3Vec, y3Vec, x1Vec, y1Vec, bridgeRadiusMin2S, bridgeRadiusMax2S);
-
-    for (int i = 0; i < 3; i++) {
-      x2Vec[i] = x5;
-      y2Vec[i] = y5;
-    }
-    if (modules.subdets()[lowerModuleIndex5] == Endcap and modules.moduleType()[lowerModuleIndex5] == TwoS) {
-      x2Vec[1] = mds.anchorLowEdgeX()[fifthMDIndex];
-      x2Vec[2] = mds.anchorHighEdgeX()[fifthMDIndex];
-
-      y2Vec[1] = mds.anchorLowEdgeY()[fifthMDIndex];
-      y2Vec[2] = mds.anchorHighEdgeY()[fifthMDIndex];
-    }
-
-    float outerRadiusMin2S, outerRadiusMax2S;
-    computeErrorInRadius(acc, x3Vec, y3Vec, x1Vec, y1Vec, x2Vec, y2Vec, outerRadiusMin2S, outerRadiusMax2S);
-
     float g, f;
     outerRadius = triplets.radius()[outerTripletIndex];
     bridgeRadius = computeRadiusFromThreeAnchorHits(acc, x2, y2, x3, y3, x4, y4, g, f);
     innerRadius = triplets.radius()[innerTripletIndex];
+
+    bool inference = lst::t5dnn::runInference(acc,
+                                              mds,
+                                              firstMDIndex,
+                                              secondMDIndex,
+                                              thirdMDIndex,
+                                              fourthMDIndex,
+                                              fifthMDIndex,
+                                              innerRadius,
+                                              outerRadius,
+                                              bridgeRadius);
+    tightCutFlag = tightCutFlag and inference;  // T5-in-TC cut
+    if (!inference)                             // T5-building cut
+      return false;
+
+    if (not runQuintupletdBetaAlgoSelector(acc,
+                                           modules,
+                                           mds,
+                                           segments,
+                                           lowerModuleIndex1,
+                                           lowerModuleIndex2,
+                                           lowerModuleIndex3,
+                                           lowerModuleIndex4,
+                                           firstSegmentIndex,
+                                           thirdSegmentIndex,
+                                           firstMDIndex,
+                                           secondMDIndex,
+                                           thirdMDIndex,
+                                           fourthMDIndex,
+                                           dBeta1,
+                                           ptCut))
+      return false;
+
+    if (not runQuintupletdBetaAlgoSelector(acc,
+                                           modules,
+                                           mds,
+                                           segments,
+                                           lowerModuleIndex1,
+                                           lowerModuleIndex2,
+                                           lowerModuleIndex4,
+                                           lowerModuleIndex5,
+                                           firstSegmentIndex,
+                                           fourthSegmentIndex,
+                                           firstMDIndex,
+                                           secondMDIndex,
+                                           fourthMDIndex,
+                                           fifthMDIndex,
+                                           dBeta2,
+                                           ptCut))
+      return false;
+
     g = triplets.centerX()[innerTripletIndex];
     f = triplets.centerY()[innerTripletIndex];
 
@@ -2194,73 +1579,15 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                tightCutFlag))
       return false;
 
-    if (innerRadius < 0.95f * ptCut / (2.f * k2Rinv1GeVf))
-      return false;
-
-    //split by category
-    bool matchedRadii;
-    if (modules.subdets()[lowerModuleIndex1] == Barrel and modules.subdets()[lowerModuleIndex2] == Barrel and
-        modules.subdets()[lowerModuleIndex3] == Barrel and modules.subdets()[lowerModuleIndex4] == Barrel and
-        modules.subdets()[lowerModuleIndex5] == Barrel) {
-      matchedRadii = matchRadiiBBBBB(acc, innerRadius, bridgeRadius, outerRadius);
-    } else if (modules.subdets()[lowerModuleIndex1] == Barrel and modules.subdets()[lowerModuleIndex2] == Barrel and
-               modules.subdets()[lowerModuleIndex3] == Barrel and modules.subdets()[lowerModuleIndex4] == Barrel and
-               modules.subdets()[lowerModuleIndex5] == Endcap) {
-      matchedRadii = matchRadiiBBBBE(acc, innerRadius, bridgeRadius, outerRadius);
-    } else if (modules.subdets()[lowerModuleIndex1] == Barrel and modules.subdets()[lowerModuleIndex2] == Barrel and
-               modules.subdets()[lowerModuleIndex3] == Barrel and modules.subdets()[lowerModuleIndex4] == Endcap and
-               modules.subdets()[lowerModuleIndex5] == Endcap) {
-      if (modules.layers()[lowerModuleIndex1] == 1) {
-        matchedRadii =
-            matchRadiiBBBEE12378(acc, innerRadius, bridgeRadius, outerRadius, bridgeRadiusMin2S, bridgeRadiusMax2S);
-      } else if (modules.layers()[lowerModuleIndex1] == 2) {
-        matchedRadii =
-            matchRadiiBBBEE23478(acc, innerRadius, bridgeRadius, outerRadius, bridgeRadiusMin2S, bridgeRadiusMax2S);
-      } else {
-        matchedRadii =
-            matchRadiiBBBEE34578(acc, innerRadius, bridgeRadius, outerRadius, bridgeRadiusMin2S, bridgeRadiusMax2S);
-      }
-    }
-
-    else if (modules.subdets()[lowerModuleIndex1] == Barrel and modules.subdets()[lowerModuleIndex2] == Barrel and
-             modules.subdets()[lowerModuleIndex3] == Endcap and modules.subdets()[lowerModuleIndex4] == Endcap and
-             modules.subdets()[lowerModuleIndex5] == Endcap) {
-      matchedRadii = matchRadiiBBEEE(acc, innerRadius, bridgeRadius, outerRadius, bridgeRadiusMin2S, bridgeRadiusMax2S);
-    } else if (modules.subdets()[lowerModuleIndex1] == Barrel and modules.subdets()[lowerModuleIndex2] == Endcap and
-               modules.subdets()[lowerModuleIndex3] == Endcap and modules.subdets()[lowerModuleIndex4] == Endcap and
-               modules.subdets()[lowerModuleIndex5] == Endcap) {
-      matchedRadii = matchRadiiBEEEE(acc,
-                                     innerRadius,
-                                     bridgeRadius,
-                                     outerRadius,
-                                     innerRadiusMin2S,
-                                     innerRadiusMax2S,
-                                     bridgeRadiusMin2S,
-                                     bridgeRadiusMax2S);
-    } else {
-      matchedRadii = matchRadiiEEEEE(acc,
-                                     innerRadius,
-                                     bridgeRadius,
-                                     outerRadius,
-                                     innerRadiusMin2S,
-                                     innerRadiusMax2S,
-                                     bridgeRadiusMin2S,
-                                     bridgeRadiusMax2S);
-    }
-
-    //compute regression radius right here - this computation is expensive!!!
-    if (not matchedRadii)
-      return false;
+    // 5 categories for sigmas
+    float sigmas2[5], delta1[5], delta2[5], slopes[5];
+    bool isFlat[5];
 
     float xVec[] = {x1, x2, x3, x4, x5};
     float yVec[] = {y1, y2, y3, y4, y5};
     const uint16_t lowerModuleIndices[] = {
         lowerModuleIndex1, lowerModuleIndex2, lowerModuleIndex3, lowerModuleIndex4, lowerModuleIndex5};
 
-    // 5 categories for sigmas
-    float sigmas2[5], delta1[5], delta2[5], slopes[5];
-    bool isFlat[5];
-
     computeSigmasForRegression(acc, modules, lowerModuleIndices, delta1, delta2, slopes, isFlat);
     regressionRadius = computeRadiusUsingRegression(acc,
                                                     Params_T5::kLayers,
@@ -2275,25 +1602,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                     sigmas2,
                                                     chiSquared);
 
-    unsigned int mdIndices[] = {firstMDIndex, secondMDIndex, thirdMDIndex, fourthMDIndex, fifthMDIndex};
-    float inference = t5dnn::runInference(acc,
-                                          modules,
-                                          mds,
-                                          segments,
-                                          triplets,
-                                          xVec,
-                                          yVec,
-                                          mdIndices,
-                                          lowerModuleIndices,
-                                          innerTripletIndex,
-                                          outerTripletIndex,
-                                          innerRadius,
-                                          outerRadius,
-                                          bridgeRadius);
-    tightCutFlag = tightCutFlag and (inference > t5dnn::kLSTWp2);  // T5-in-TC cut
-    if (inference <= t5dnn::kLSTWp2)                               // T5-building cut
-      return false;
-
     //compute the other chisquared
     //non anchor is always shifted for tilted and endcap!
     float nonAnchorDelta1[Params_T5::kLayers], nonAnchorDelta2[Params_T5::kLayers], nonAnchorSlopes[Params_T5::kLayers];
@@ -2342,7 +1650,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                   Quintuplets quintuplets,
                                   QuintupletsOccupancy quintupletsOccupancy,
                                   ObjectRangesConst ranges,
-                                  uint16_t nEligibleT5Modules) const {
+                                  uint16_t nEligibleT5Modules,
+                                  const float ptCut) const {
       auto const globalThreadIdx = alpaka::getIdx<alpaka::Grid, alpaka::Threads>(acc);
       auto const gridThreadExtent = alpaka::getWorkDiv<alpaka::Grid, alpaka::Threads>(acc);
 
@@ -2373,7 +1682,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
             uint16_t lowerModule5 = triplets.lowerModuleIndices()[outerTripletIndex][2];
 
             float innerRadius, outerRadius, bridgeRadius, regressionG, regressionF, regressionRadius, rzChiSquared,
-                chiSquared, nonAnchorChiSquared;  //required for making distributions
+                chiSquared, nonAnchorChiSquared, dBeta1, dBeta2;  //required for making distributions
 
             bool tightCutFlag = false;
             bool success = runQuintupletDefaultAlgo(acc,
@@ -2397,14 +1706,19 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                     rzChiSquared,
                                                     chiSquared,
                                                     nonAnchorChiSquared,
-                                                    tightCutFlag);
+                                                    dBeta1,
+                                                    dBeta2,
+                                                    tightCutFlag,
+                                                    ptCut);
 
             if (success) {
               int totOccupancyQuintuplets = alpaka::atomicAdd(
                   acc, &quintupletsOccupancy.totOccupancyQuintuplets()[lowerModule1], 1u, alpaka::hierarchy::Threads{});
               if (totOccupancyQuintuplets >= ranges.quintupletModuleOccupancy()[lowerModule1]) {
 #ifdef WARNINGS
-                printf("Quintuplet excess alert! Module index = %d\n", lowerModule1);
+                printf("Quintuplet excess alert! Module index = %d, Occupancy = %d\n",
+                       lowerModule1,
+                       totOccupancyQuintuplets);
 #endif
               } else {
                 int quintupletModuleIndex = alpaka::atomicAdd(
@@ -2440,6 +1754,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                         rzChiSquared,
                                         chiSquared,
                                         nonAnchorChiSquared,
+                                        dBeta1,
+                                        dBeta2,
                                         pt,
                                         eta,
                                         phi,
@@ -2464,7 +1780,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     ALPAKA_FN_ACC void operator()(TAcc const& acc,
                                   ModulesConst modules,
                                   TripletsOccupancyConst tripletsOccupancy,
-                                  ObjectRanges ranges) const {
+                                  ObjectRanges ranges,
+                                  const float ptCut) const {
       // implementation is 1D with a single block
       static_assert(std::is_same_v<TAcc, ALPAKA_ACCELERATOR_NAMESPACE::Acc1D>, "Should be Acc1D");
       ALPAKA_ASSERT_ACC((alpaka::getWorkDiv<alpaka::Grid, alpaka::Blocks>(acc)[0] == 1));
@@ -2481,6 +1798,25 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
       }
       alpaka::syncBlockThreads(acc);
 
+      // Occupancy matrix for 0.8 GeV pT Cut
+      constexpr int p08_occupancy_matrix[4][4] = {
+          {336, 414, 231, 146},  // category 0
+          {0, 0, 0, 0},          // category 1
+          {0, 0, 0, 0},          // category 2
+          {0, 0, 191, 106}       // category 3
+      };
+
+      // Occupancy matrix for 0.6 GeV pT Cut, 99.99%
+      constexpr int p06_occupancy_matrix[4][4] = {
+          {325, 237, 217, 176},  // category 0
+          {0, 0, 0, 0},          // category 1
+          {0, 0, 0, 0},          // category 2
+          {0, 0, 129, 180}       // category 3
+      };
+
+      // Select the appropriate occupancy matrix based on ptCut
+      const auto& occupancy_matrix = (ptCut < 0.8f) ? p06_occupancy_matrix : p08_occupancy_matrix;
+
       for (int i = globalThreadIdx[0]; i < modules.nLowerModules(); i += gridThreadExtent[0]) {
         // Condition for a quintuple to exist for a module
         // TCs don't exist for layers 5 and 6 barrel, and layers 2,3,4,5 endcap
@@ -2498,55 +1834,18 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
 
         int nEligibleT5Modules = alpaka::atomicAdd(acc, &nEligibleT5Modulesx, 1, alpaka::hierarchy::Threads{});
 
-        int category_number;
-        if (module_layers <= 3 && module_subdets == 5)
-          category_number = 0;
-        else if (module_layers >= 4 && module_subdets == 5)
-          category_number = 1;
-        else if (module_layers <= 2 && module_subdets == 4 && module_rings >= 11)
-          category_number = 2;
-        else if (module_layers >= 3 && module_subdets == 4 && module_rings >= 8)
-          category_number = 2;
-        else if (module_layers <= 2 && module_subdets == 4 && module_rings <= 10)
-          category_number = 3;
-        else if (module_layers >= 3 && module_subdets == 4 && module_rings <= 7)
-          category_number = 3;
-        else
-          category_number = -1;
-
-        int eta_number;
-        if (module_eta < 0.75f)
-          eta_number = 0;
-        else if (module_eta < 1.5f)
-          eta_number = 1;
-        else if (module_eta < 2.25f)
-          eta_number = 2;
-        else if (module_eta < 3.0f)
-          eta_number = 3;
-        else
-          eta_number = -1;
-
-        int occupancy;
-        if (category_number == 0 && eta_number == 0)
-          occupancy = 336;
-        else if (category_number == 0 && eta_number == 1)
-          occupancy = 414;
-        else if (category_number == 0 && eta_number == 2)
-          occupancy = 231;
-        else if (category_number == 0 && eta_number == 3)
-          occupancy = 146;
-        else if (category_number == 3 && eta_number == 1)
-          occupancy = 0;
-        else if (category_number == 3 && eta_number == 2)
-          occupancy = 191;
-        else if (category_number == 3 && eta_number == 3)
-          occupancy = 106;
-        else {
-          occupancy = 0;
+        int category_number = getCategoryNumber(module_layers, module_subdets, module_rings);
+        int eta_number = getEtaBin(module_eta);
+
+        int occupancy = 0;
+        if (category_number != -1 && eta_number != -1) {
+          occupancy = occupancy_matrix[category_number][eta_number];
+        }
 #ifdef WARNINGS
+        else {
           printf("Unhandled case in createEligibleModulesListForQuintupletsGPU! Module index = %i\n", i);
-#endif
         }
+#endif
 
         int nTotQ = alpaka::atomicAdd(acc, &nTotalQuintupletsx, occupancy, alpaka::hierarchy::Threads{});
         ranges.quintupletModuleIndices()[i] = nTotQ;
diff --git a/RecoTracker/LSTCore/src/alpaka/Segment.h b/RecoTracker/LSTCore/src/alpaka/Segment.h
index fc885e9d66afe..911119bf67ff8 100644
--- a/RecoTracker/LSTCore/src/alpaka/Segment.h
+++ b/RecoTracker/LSTCore/src/alpaka/Segment.h
@@ -39,46 +39,26 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
   }
 
   ALPAKA_FN_ACC ALPAKA_FN_INLINE float moduleGapSize_seg(short layer, short ring, short subdet, short side, short rod) {
-    static constexpr float miniDeltaTilted[3] = {0.26f, 0.26f, 0.26f};
-    static constexpr float miniDeltaFlat[6] = {0.26f, 0.16f, 0.16f, 0.18f, 0.18f, 0.18f};
-    static constexpr float miniDeltaLooseTilted[3] = {0.4f, 0.4f, 0.4f};
-    static constexpr float miniDeltaEndcap[5][15] = {
-        {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
-        {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
-        {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.18f, 0.18f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
-        {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.18f, 0.18f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
-        {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.18f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f}};
-
     unsigned int iL = layer - 1;
     unsigned int iR = ring - 1;
 
     float moduleSeparation = 0;
 
     if (subdet == Barrel and side == Center) {
-      moduleSeparation = miniDeltaFlat[iL];
+      moduleSeparation = kMiniDeltaFlat[iL];
     } else if (isTighterTiltedModules_seg(subdet, layer, side, rod)) {
-      moduleSeparation = miniDeltaTilted[iL];
+      moduleSeparation = kMiniDeltaTilted[iL];
     } else if (subdet == Endcap) {
-      moduleSeparation = miniDeltaEndcap[iL][iR];
+      moduleSeparation = kMiniDeltaEndcap[iL][iR];
     } else  //Loose tilted modules
     {
-      moduleSeparation = miniDeltaLooseTilted[iL];
+      moduleSeparation = kMiniDeltaLooseTilted[iL];
     }
 
     return moduleSeparation;
   }
 
   ALPAKA_FN_ACC ALPAKA_FN_INLINE float moduleGapSize_seg(ModulesConst modules, unsigned int moduleIndex) {
-    static constexpr float miniDeltaTilted[3] = {0.26f, 0.26f, 0.26f};
-    static constexpr float miniDeltaFlat[6] = {0.26f, 0.16f, 0.16f, 0.18f, 0.18f, 0.18f};
-    static constexpr float miniDeltaLooseTilted[3] = {0.4f, 0.4f, 0.4f};
-    static constexpr float miniDeltaEndcap[5][15] = {
-        {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
-        {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
-        {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.18f, 0.18f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
-        {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.18f, 0.18f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f},
-        {0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.4f, 0.18f, /*10*/ 0.18f, 0.18f, 0.18f, 0.18f, 0.18f}};
-
     unsigned int iL = modules.layers()[moduleIndex] - 1;
     unsigned int iR = modules.rings()[moduleIndex] - 1;
     short subdet = modules.subdets()[moduleIndex];
@@ -87,14 +67,14 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     float moduleSeparation = 0;
 
     if (subdet == Barrel and side == Center) {
-      moduleSeparation = miniDeltaFlat[iL];
+      moduleSeparation = kMiniDeltaFlat[iL];
     } else if (isTighterTiltedModules_seg(modules, moduleIndex)) {
-      moduleSeparation = miniDeltaTilted[iL];
+      moduleSeparation = kMiniDeltaTilted[iL];
     } else if (subdet == Endcap) {
-      moduleSeparation = miniDeltaEndcap[iL][iR];
+      moduleSeparation = kMiniDeltaEndcap[iL][iR];
     } else  //Loose tilted modules
     {
-      moduleSeparation = miniDeltaLooseTilted[iL];
+      moduleSeparation = kMiniDeltaLooseTilted[iL];
     }
 
     return moduleSeparation;
@@ -116,7 +96,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                       uint16_t innerLowerModuleIndex,
                                                       uint16_t outerLowerModuleIndex,
                                                       unsigned int innerMDIndex,
-                                                      unsigned int outerMDIndex) {
+                                                      unsigned int outerMDIndex,
+                                                      const float ptCut) {
     float sdMuls = (modules.subdets()[innerLowerModuleIndex] == Barrel)
                        ? kMiniMulsPtScaleBarrel[modules.layers()[innerLowerModuleIndex] - 1] * 3.f / ptCut
                        : kMiniMulsPtScaleEndcap[modules.layers()[innerLowerModuleIndex] - 1] * 3.f / ptCut;
@@ -299,7 +280,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                   float& dPhiMax,
                                                                   float& dPhiChange,
                                                                   float& dPhiChangeMin,
-                                                                  float& dPhiChangeMax) {
+                                                                  float& dPhiChangeMax,
+                                                                  const float ptCut) {
     float sdMuls = (modules.subdets()[innerLowerModuleIndex] == Barrel)
                        ? kMiniMulsPtScaleBarrel[modules.layers()[innerLowerModuleIndex] - 1] * 3.f / ptCut
                        : kMiniMulsPtScaleEndcap[modules.layers()[innerLowerModuleIndex] - 1] * 3.f / ptCut;
@@ -357,7 +339,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                     innerLowerModuleIndex,
                     outerLowerModuleIndex,
                     innerMDIndex,
-                    outerMDIndex);
+                    outerMDIndex,
+                    ptCut);
 
     float innerMDAlpha = mds.dphichanges()[innerMDIndex];
     float outerMDAlpha = mds.dphichanges()[outerMDIndex];
@@ -389,7 +372,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                   float& dPhiMax,
                                                                   float& dPhiChange,
                                                                   float& dPhiChangeMin,
-                                                                  float& dPhiChangeMax) {
+                                                                  float& dPhiChangeMax,
+                                                                  const float ptCut) {
     float xIn, yIn, zIn, rtIn, xOut, yOut, zOut, rtOut;
 
     xIn = mds.anchorX()[innerMDIndex];
@@ -470,7 +454,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                     innerLowerModuleIndex,
                     outerLowerModuleIndex,
                     innerMDIndex,
-                    outerMDIndex);
+                    outerMDIndex,
+                    ptCut);
 
     float innerMDAlpha = mds.dphichanges()[innerMDIndex];
     float outerMDAlpha = mds.dphichanges()[outerMDIndex];
@@ -502,7 +487,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                             float& dPhiMax,
                                                             float& dPhiChange,
                                                             float& dPhiChangeMin,
-                                                            float& dPhiChangeMax) {
+                                                            float& dPhiChangeMax,
+                                                            const float ptCut) {
     if (modules.subdets()[innerLowerModuleIndex] == Barrel and modules.subdets()[outerLowerModuleIndex] == Barrel) {
       return runSegmentDefaultAlgoBarrel(acc,
                                          modules,
@@ -516,7 +502,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                          dPhiMax,
                                          dPhiChange,
                                          dPhiChangeMin,
-                                         dPhiChangeMax);
+                                         dPhiChangeMax,
+                                         ptCut);
     } else {
       return runSegmentDefaultAlgoEndcap(acc,
                                          modules,
@@ -530,7 +517,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                          dPhiMax,
                                          dPhiChange,
                                          dPhiChangeMin,
-                                         dPhiChangeMax);
+                                         dPhiChangeMax,
+                                         ptCut);
     }
   }
 
@@ -542,7 +530,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                   MiniDoubletsOccupancyConst mdsOccupancy,
                                   Segments segments,
                                   SegmentsOccupancy segmentsOccupancy,
-                                  ObjectRangesConst ranges) const {
+                                  ObjectRangesConst ranges,
+                                  const float ptCut) const {
       auto const globalBlockIdx = alpaka::getIdx<alpaka::Grid, alpaka::Blocks>(acc);
       auto const blockThreadIdx = alpaka::getIdx<alpaka::Block, alpaka::Threads>(acc);
       auto const gridBlockExtent = alpaka::getWorkDiv<alpaka::Grid, alpaka::Blocks>(acc);
@@ -595,7 +584,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                       dPhiMax,
                                       dPhiChange,
                                       dPhiChangeMin,
-                                      dPhiChangeMax)) {
+                                      dPhiChangeMax,
+                                      ptCut)) {
               unsigned int totOccupancySegments =
                   alpaka::atomicAdd(acc,
                                     &segmentsOccupancy.totOccupancySegments()[innerLowerModuleIndex],
@@ -603,7 +593,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                     alpaka::hierarchy::Threads{});
               if (static_cast<int>(totOccupancySegments) >= ranges.segmentModuleOccupancy()[innerLowerModuleIndex]) {
 #ifdef WARNINGS
-                printf("Segment excess alert! Module index = %d\n", innerLowerModuleIndex);
+                printf("Segment excess alert! Module index = %d, Occupancy = %d\n",
+                       innerLowerModuleIndex,
+                       totOccupancySegments);
 #endif
               } else {
                 unsigned int segmentModuleIdx = alpaka::atomicAdd(
@@ -634,10 +626,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
 
   struct CreateSegmentArrayRanges {
     template <typename TAcc>
-    ALPAKA_FN_ACC void operator()(TAcc const& acc,
-                                  ModulesConst modules,
-                                  ObjectRanges ranges,
-                                  MiniDoubletsConst mds) const {
+    ALPAKA_FN_ACC void operator()(
+        TAcc const& acc, ModulesConst modules, ObjectRanges ranges, MiniDoubletsConst mds, const float ptCut) const {
       // implementation is 1D with a single block
       static_assert(std::is_same_v<TAcc, ALPAKA_ACCELERATOR_NAMESPACE::Acc1D>, "Should be Acc1D");
       ALPAKA_ASSERT_ACC((alpaka::getWorkDiv<alpaka::Grid, alpaka::Blocks>(acc)[0] == 1));
@@ -652,6 +642,25 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
       }
       alpaka::syncBlockThreads(acc);
 
+      // Occupancy matrix for 0.8 GeV pT Cut
+      constexpr int p08_occupancy_matrix[4][4] = {
+          {572, 300, 183, 62},  // category 0
+          {191, 128, 0, 0},     // category 1
+          {0, 107, 102, 0},     // category 2
+          {0, 64, 79, 85}       // category 3
+      };
+
+      // Occupancy matrix for 0.6 GeV pT Cut, 99.9%
+      constexpr int p06_occupancy_matrix[4][4] = {
+          {936, 351, 256, 61},  // category 0
+          {1358, 763, 0, 0},    // category 1
+          {0, 210, 268, 0},     // category 2
+          {0, 60, 97, 96}       // category 3
+      };
+
+      // Select the appropriate occupancy matrix based on ptCut
+      const auto& occupancy_matrix = (ptCut < 0.8f) ? p06_occupancy_matrix : p08_occupancy_matrix;
+
       for (uint16_t i = globalThreadIdx[0]; i < modules.nLowerModules(); i += gridThreadExtent[0]) {
         if (modules.nConnectedModules()[i] == 0) {
           ranges.segmentModuleIndices()[i] = nTotalSegments;
@@ -664,63 +673,18 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
         short module_subdets = modules.subdets()[i];
         float module_eta = alpaka::math::abs(acc, modules.eta()[i]);
 
-        int category_number;
-        if (module_layers <= 3 && module_subdets == 5)
-          category_number = 0;
-        else if (module_layers >= 4 && module_subdets == 5)
-          category_number = 1;
-        else if (module_layers <= 2 && module_subdets == 4 && module_rings >= 11)
-          category_number = 2;
-        else if (module_layers >= 3 && module_subdets == 4 && module_rings >= 8)
-          category_number = 2;
-        else if (module_layers <= 2 && module_subdets == 4 && module_rings <= 10)
-          category_number = 3;
-        else if (module_layers >= 3 && module_subdets == 4 && module_rings <= 7)
-          category_number = 3;
-        else
-          category_number = -1;
-
-        int eta_number;
-        if (module_eta < 0.75f)
-          eta_number = 0;
-        else if (module_eta < 1.5f)
-          eta_number = 1;
-        else if (module_eta < 2.25f)
-          eta_number = 2;
-        else if (module_eta < 3.0f)
-          eta_number = 3;
-        else
-          eta_number = -1;
-
-        int occupancy;
-        if (category_number == 0 && eta_number == 0)
-          occupancy = 572;
-        else if (category_number == 0 && eta_number == 1)
-          occupancy = 300;
-        else if (category_number == 0 && eta_number == 2)
-          occupancy = 183;
-        else if (category_number == 0 && eta_number == 3)
-          occupancy = 62;
-        else if (category_number == 1 && eta_number == 0)
-          occupancy = 191;
-        else if (category_number == 1 && eta_number == 1)
-          occupancy = 128;
-        else if (category_number == 2 && eta_number == 1)
-          occupancy = 107;
-        else if (category_number == 2 && eta_number == 2)
-          occupancy = 102;
-        else if (category_number == 3 && eta_number == 1)
-          occupancy = 64;
-        else if (category_number == 3 && eta_number == 2)
-          occupancy = 79;
-        else if (category_number == 3 && eta_number == 3)
-          occupancy = 85;
-        else {
-          occupancy = 0;
+        int category_number = getCategoryNumber(module_layers, module_subdets, module_rings);
+        int eta_number = getEtaBin(module_eta);
+
+        int occupancy = 0;
+        if (category_number != -1 && eta_number != -1) {
+          occupancy = occupancy_matrix[category_number][eta_number];
+        }
 #ifdef WARNINGS
+        else {
           printf("Unhandled case in createSegmentArrayRanges! Module index = %i\n", i);
-#endif
         }
+#endif
 
         int nTotSegs = alpaka::atomicAdd(acc, &nTotalSegments, occupancy, alpaka::hierarchy::Threads{});
         ranges.segmentModuleIndices()[i] = nTotSegs;
diff --git a/RecoTracker/LSTCore/src/alpaka/Triplet.h b/RecoTracker/LSTCore/src/alpaka/Triplet.h
index 9192edbd9a186..ae2faecb080a6 100644
--- a/RecoTracker/LSTCore/src/alpaka/Triplet.h
+++ b/RecoTracker/LSTCore/src/alpaka/Triplet.h
@@ -2,6 +2,7 @@
 #define RecoTracker_LSTCore_src_alpaka_Triplet_h
 
 #include "HeterogeneousCore/AlpakaInterface/interface/workdivision.h"
+#include "FWCore/Utilities/interface/isFinite.h"
 
 #include "RecoTracker/LSTCore/interface/alpaka/Common.h"
 #include "RecoTracker/LSTCore/interface/ModulesSoA.h"
@@ -76,54 +77,295 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                        uint16_t outerOuterLowerModuleIndex,
                                                        unsigned int firstMDIndex,
                                                        unsigned int secondMDIndex,
-                                                       unsigned int thirdMDIndex) {
-    //get the rt and z
-    const float& r1 = mds.anchorRt()[firstMDIndex];
-    const float& r2 = mds.anchorRt()[secondMDIndex];
-    const float& r3 = mds.anchorRt()[thirdMDIndex];
-
-    const float& z1 = mds.anchorZ()[firstMDIndex];
-    const float& z2 = mds.anchorZ()[secondMDIndex];
-    const float& z3 = mds.anchorZ()[thirdMDIndex];
-
+                                                       unsigned int thirdMDIndex,
+                                                       float circleRadius,
+                                                       float circleCenterX,
+                                                       float circleCenterY) {
     // Using lst_layer numbering convention defined in ModuleMethods.h
     const int layer1 = modules.lstLayers()[innerInnerLowerModuleIndex];
     const int layer2 = modules.lstLayers()[middleLowerModuleIndex];
     const int layer3 = modules.lstLayers()[outerOuterLowerModuleIndex];
 
-    const float residual = z2 - ((z3 - z1) / (r3 - r1) * (r2 - r1) + z1);
+    //all the values are stored in the unit of cm, in the calculation below we need to be cautious if we want to use the meter unit
+    //get r and z
+    const float r1 = mds.anchorRt()[firstMDIndex] / 100;
+    const float r2 = mds.anchorRt()[secondMDIndex] / 100;
+    const float r3 = mds.anchorRt()[thirdMDIndex] / 100;
+
+    const float z1 = mds.anchorZ()[firstMDIndex] / 100;
+    const float z2 = mds.anchorZ()[secondMDIndex] / 100;
+    const float z3 = mds.anchorZ()[thirdMDIndex] / 100;
+
+    //use linear approximation for regions 9 and 20-24 because it works better (see https://github.com/SegmentLinking/cmssw/pull/92)
+    float residual = alpaka::math::abs(acc, z2 - ((z3 - z1) / (r3 - r1) * (r2 - r1) + z1));
+
+    //region definitions: https://github.com/user-attachments/assets/2b3c1425-66eb-4524-83de-deb6f3b31f71
+    if (layer1 == 1 && layer2 == 7) {
+      return residual < 0.01f;  // Region 9
+    } else if (layer1 == 3 && layer2 == 4) {
+      if (layer3 == 5) {
+        return residual < 0.037127972f;  // Region 20
+      } else if (layer3 == 12) {
+        return residual < 0.05f;  // Region 21
+      }
+    } else if (layer1 == 4) {
+      if (layer2 == 12) {
+        return residual < 0.063831687f;  // Region 22
+      } else if (layer2 == 5) {
+        if (layer3 == 6) {
+          return residual < 0.04362525f;  // Region 23
+        } else if (layer3 == 12) {
+          return residual < 0.05f;  // Region 24
+        }
+      }
+    }
 
-    if (layer1 == 12 and layer2 == 13 and layer3 == 14) {
-      return false;
-    } else if (layer1 == 1 and layer2 == 2 and layer3 == 3) {
-      return alpaka::math::abs(acc, residual) < 0.53f;
-    } else if (layer1 == 1 and layer2 == 2 and layer3 == 7) {
-      return alpaka::math::abs(acc, residual) < 1;
-    } else if (layer1 == 13 and layer2 == 14 and layer3 == 15) {
-      return false;
-    } else if (layer1 == 14 and layer2 == 15 and layer3 == 16) {
-      return false;
-    } else if (layer1 == 1 and layer2 == 7 and layer3 == 8) {
-      return alpaka::math::abs(acc, residual) < 1;
-    } else if (layer1 == 2 and layer2 == 3 and layer3 == 4) {
-      return alpaka::math::abs(acc, residual) < 1.21f;
-    } else if (layer1 == 2 and layer2 == 3 and layer3 == 7) {
-      return alpaka::math::abs(acc, residual) < 1.f;
-    } else if (layer1 == 2 and layer2 == 7 and layer3 == 8) {
-      return alpaka::math::abs(acc, residual) < 1.f;
-    } else if (layer1 == 3 and layer2 == 4 and layer3 == 5) {
-      return alpaka::math::abs(acc, residual) < 2.7f;
-    } else if (layer1 == 4 and layer2 == 5 and layer3 == 6) {
-      return alpaka::math::abs(acc, residual) < 3.06f;
-    } else if (layer1 == 7 and layer2 == 8 and layer3 == 9) {
-      return alpaka::math::abs(acc, residual) < 1;
-    } else if (layer1 == 8 and layer2 == 9 and layer3 == 10) {
-      return alpaka::math::abs(acc, residual) < 1;
-    } else if (layer1 == 9 and layer2 == 10 and layer3 == 11) {
-      return alpaka::math::abs(acc, residual) < 1;
+    //get the type of module: 0 is ps, 1 is 2s
+    const int moduleType3 = modules.moduleType()[outerOuterLowerModuleIndex];
+
+    //get the x,y position of each MD
+    const float x1 = mds.anchorX()[firstMDIndex] / 100;
+    const float x2 = mds.anchorX()[secondMDIndex] / 100;
+    const float x3 = mds.anchorX()[thirdMDIndex] / 100;
+
+    const float y1 = mds.anchorY()[firstMDIndex] / 100;
+    const float y2 = mds.anchorY()[secondMDIndex] / 100;
+    const float y3 = mds.anchorY()[thirdMDIndex] / 100;
+
+    //set initial and target points
+    float x_init = x2;
+    float y_init = y2;
+    float z_init = z2;
+    float r_init = r2;
+
+    float z_target = z3;
+    float r_target = r3;
+
+    float x_other = x1;
+    float y_other = y1;
+
+    float dz = z2 - z1;
+
+    //use MD2 for regions 5 and 19 because it works better (see https://github.com/SegmentLinking/cmssw/pull/92)
+    if ((layer1 == 8 && layer2 == 14 && layer3 == 15) || (layer1 == 3 && layer2 == 12 && layer3 == 13)) {
+      x_init = x1;
+      y_init = y1;
+      z_init = z1;
+      r_init = r1;
+
+      z_target = z2;
+      r_target = r2;
+
+      x_other = x3;
+      y_other = y3;
+
+      dz = z3 - z1;
+    }
+
+    //use the 3 MDs to fit a circle. This is the circle parameters, for circle centers and circle radius
+    float x_center = circleCenterX / 100;
+    float y_center = circleCenterY / 100;
+    float pt = 2 * k2Rinv1GeVf * circleRadius;  //k2Rinv1GeVf is already in cm^(-1)
+
+    //determine the charge
+    int charge = 0;
+    if ((x2 - x1) * (y3 - y1) - (y2 - y1) * (x3 - x1) > 0)
+      charge = -1;
+    else
+      charge = 1;
+
+    //get the absolute value of px and py at the initial point
+    float px = 2 * k2Rinv1GeVf * alpaka::math::abs(acc, (y_init - y_center)) * 100;
+    float py = 2 * k2Rinv1GeVf * alpaka::math::abs(acc, (x_init - x_center)) * 100;
+
+    //Above line only gives you the correct value of px and py, but signs of px and py calculated below.
+    //We look at if the circle is clockwise or anti-clock wise, to make it simpler, we separate the x-y plane into 4 quarters.
+    if (x_init > x_center && y_init > y_center)  //1st quad
+    {
+      if (charge == 1)
+        py = -py;
+      if (charge == -1)
+        px = -px;
+    }
+    if (x_init < x_center && y_init > y_center)  //2nd quad
+    {
+      if (charge == -1) {
+        px = -px;
+        py = -py;
+      }
+    }
+    if (x_init < x_center && y_init < y_center)  //3rd quad
+    {
+      if (charge == 1)
+        px = -px;
+      if (charge == -1)
+        py = -py;
+    }
+    if (x_init > x_center && y_init < y_center)  //4th quad
+    {
+      if (charge == 1) {
+        px = -px;
+        py = -py;
+      }
+    }
+
+    //But if the initial T3 curve goes across quarters(i.e. cross axis to separate the quarters), need special redeclaration of px,py signs on these to avoid errors
+    if (x3 < x2 && x2 < x1)
+      px = -alpaka::math::abs(acc, px);
+    else if (x3 > x2 && x2 > x1)
+      px = alpaka::math::abs(acc, px);
+    if (y3 < y2 && y2 < y1)
+      py = -alpaka::math::abs(acc, py);
+    else if (y3 > y2 && y2 > y1)
+      py = alpaka::math::abs(acc, py);
+
+    float AO = alpaka::math::sqrt(
+        acc, (x_other - x_center) * (x_other - x_center) + (y_other - y_center) * (y_other - y_center));
+    float BO =
+        alpaka::math::sqrt(acc, (x_init - x_center) * (x_init - x_center) + (y_init - y_center) * (y_init - y_center));
+    float AB2 = (x_other - x_init) * (x_other - x_init) + (y_other - y_init) * (y_other - y_init);
+    float dPhi = alpaka::math::acos(acc, (AO * AO + BO * BO - AB2) / (2 * AO * BO));  //Law of Cosines
+    float ds = circleRadius / 100 * dPhi;
+    float pz = dz / ds * pt;
+
+    float p = alpaka::math::sqrt(acc, px * px + py * py + pz * pz);
+    float a = -2.f * k2Rinv1GeVf * 100 * charge;
+    float rou = a / p;
+
+    float rzChiSquared = 0;
+    float error = 0;
+
+    //check the tilted module, side: PosZ, NegZ, Center(for not tilted)
+    float drdz = alpaka::math::abs(acc, modules.drdzs()[outerOuterLowerModuleIndex]);
+    short side = modules.sides()[outerOuterLowerModuleIndex];
+    short subdets = modules.subdets()[outerOuterLowerModuleIndex];
+
+    //calculate residual
+    if (layer3 <= 6 && ((side == lst::Center) or (drdz < 1))) {  // for barrel
+      float paraA = r_init * r_init + 2 * (px * px + py * py) / (a * a) + 2 * (y_init * px - x_init * py) / a -
+                    r_target * r_target;
+      float paraB = 2 * (x_init * px + y_init * py) / a;
+      float paraC = 2 * (y_init * px - x_init * py) / a + 2 * (px * px + py * py) / (a * a);
+      float A = paraB * paraB + paraC * paraC;
+      float B = 2 * paraA * paraB;
+      float C = paraA * paraA - paraC * paraC;
+      float sol1 = (-B + alpaka::math::sqrt(acc, B * B - 4 * A * C)) / (2 * A);
+      float sol2 = (-B - alpaka::math::sqrt(acc, B * B - 4 * A * C)) / (2 * A);
+      float solz1 = alpaka::math::asin(acc, sol1) / rou * pz / p + z_init;
+      float solz2 = alpaka::math::asin(acc, sol2) / rou * pz / p + z_init;
+      float diffz1 = (solz1 - z_target) * 100;
+      float diffz2 = (solz2 - z_target) * 100;
+      if (edm::isNotFinite(diffz1))
+        residual = diffz2;
+      else if (edm::isNotFinite(diffz2))
+        residual = diffz1;
+      else {
+        residual = (alpaka::math::abs(acc, diffz1) < alpaka::math::abs(acc, diffz2)) ? diffz1 : diffz2;
+      }
+    } else {  // for endcap
+      float s = (z_target - z_init) * p / pz;
+      float x = x_init + px / a * alpaka::math::sin(acc, rou * s) - py / a * (1 - alpaka::math::cos(acc, rou * s));
+      float y = y_init + py / a * alpaka::math::sin(acc, rou * s) + px / a * (1 - alpaka::math::cos(acc, rou * s));
+      residual = (r_target - alpaka::math::sqrt(acc, x * x + y * y)) * 100;
+    }
+
+    // error
+    if (moduleType3 == 0) {
+      error = 0.15f;  //PS
     } else {
-      return alpaka::math::abs(acc, residual) < 5;
+      error = 5.0f;  //2S
     }
+
+    float projection_missing2 = 1;
+    if (drdz < 1)
+      projection_missing2 = ((subdets == lst::Endcap) or (side == lst::Center))
+                                ? 1.f
+                                : 1 / (1 + drdz * drdz);  // cos(atan(drdz)), if dr/dz<1
+    if (drdz > 1)
+      projection_missing2 = ((subdets == lst::Endcap) or (side == lst::Center))
+                                ? 1.f
+                                : drdz * drdz / (1 + drdz * drdz);  //sin(atan(drdz)), if dr/dz>1
+
+    rzChiSquared = 12 * (residual * residual) / (error * error * projection_missing2);
+
+    //helix calculation returns NaN, use linear approximation
+    if (edm::isNotFinite(rzChiSquared) || circleRadius < 0) {
+      float slope = (z3 - z1) / (r3 - r1);
+
+      residual = (layer3 <= 6) ? ((z3 - z1) - slope * (r3 - r1)) : ((r3 - r1) - (z3 - z1) / slope);
+      residual = (moduleType3 == 0) ? residual / 0.15f : residual / 5.0f;
+
+      rzChiSquared = 12 * residual * residual;
+      return rzChiSquared < 2.8e-4;
+    }
+
+    //cuts for different regions
+    //region definitions: https://github.com/user-attachments/assets/2b3c1425-66eb-4524-83de-deb6f3b31f71
+    //for the logic behind the cuts, see https://github.com/SegmentLinking/cmssw/pull/92
+    if (layer1 == 7) {
+      if (layer2 == 8) {
+        if (layer3 == 9) {
+          return rzChiSquared < 65.47191f;  // Region 0
+        } else if (layer3 == 14) {
+          return rzChiSquared < 3.3200853f;  // Region 1
+        }
+      } else if (layer2 == 13) {
+        return rzChiSquared < 17.194584f;  // Region 2
+      }
+    } else if (layer1 == 8) {
+      if (layer2 == 9) {
+        if (layer3 == 10) {
+          return rzChiSquared < 114.91959f;  // Region 3
+        } else if (layer3 == 15) {
+          return rzChiSquared < 3.4359624f;  // Region 4
+        }
+      } else if (layer2 == 14) {
+        return rzChiSquared < 4.6487956f;  // Region 5
+      }
+    } else if (layer1 == 9) {
+      if (layer2 == 10) {
+        if (layer3 == 11) {
+          return rzChiSquared < 97.34339f;  // Region 6
+        } else if (layer3 == 16) {
+          return rzChiSquared < 3.095819f;  // Region 7
+        }
+      } else if (layer2 == 15) {
+        return rzChiSquared < 11.477617f;  // Region 8
+      }
+    } else if (layer1 == 1) {
+      if (layer3 == 7) {
+        return rzChiSquared < 96.949936f;  // Region 10
+      } else if (layer3 == 3) {
+        return rzChiSquared < 458.43982f;  // Region 11
+      }
+    } else if (layer1 == 2) {
+      if (layer2 == 7) {
+        if (layer3 == 8) {
+          return rzChiSquared < 218.82303f;  // Region 12
+        } else if (layer3 == 13) {
+          return rzChiSquared < 3.155554f;  // Region 13
+        }
+      } else if (layer2 == 3) {
+        if (layer3 == 7) {
+          return rzChiSquared < 235.5005f;  // Region 14
+        } else if (layer3 == 12) {
+          return rzChiSquared < 3.8522234f;  // Region 15
+        } else if (layer3 == 4) {
+          return rzChiSquared < 3.5852437f;  // Region 16
+        }
+      }
+    } else if (layer1 == 3) {
+      if (layer2 == 7) {
+        if (layer3 == 8) {
+          return rzChiSquared < 42.68f;  // Region 17
+        } else if (layer3 == 13) {
+          return rzChiSquared < 3.853796f;  // Region 18
+        }
+      } else if (layer2 == 12) {
+        return rzChiSquared < 6.2774787f;  // Region 19
+      }
+    }
+    return false;
   }
 
   template <typename TAcc>
@@ -141,62 +383,11 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                 float& rtOut,
                                                                 unsigned int innerSegmentIndex,
                                                                 float& betaIn,
-                                                                float& betaInCut) {
-    bool isPSIn = (modules.moduleType()[innerInnerLowerModuleIndex] == PS);
-    bool isPSOut = (modules.moduleType()[outerOuterLowerModuleIndex] == PS);
-
+                                                                float& betaInCut,
+                                                                const float ptCut) {
     float rtIn = mds.anchorRt()[firstMDIndex];
     float rtMid = mds.anchorRt()[secondMDIndex];
-    rtOut = mds.anchorRt()[thirdMDIndex];
-
-    float zIn = mds.anchorZ()[firstMDIndex];
-    float zMid = mds.anchorZ()[secondMDIndex];
-    zOut = mds.anchorZ()[thirdMDIndex];
-
-    float alpha1GeVOut = alpaka::math::asin(acc, alpaka::math::min(acc, rtOut * k2Rinv1GeVf / ptCut, kSinAlphaMax));
-
-    float rtRatio_OutIn = rtOut / rtIn;  // Outer segment beginning rt divided by inner segment beginning rt;
-    float dzDrtScale = alpaka::math::tan(acc, alpha1GeVOut) / alpha1GeVOut;  // The track can bend in r-z plane slightly
-    float zpitchIn = (isPSIn ? kPixelPSZpitch : kStrip2SZpitch);
-    float zpitchOut = (isPSOut ? kPixelPSZpitch : kStrip2SZpitch);
-
-    const float zHi =
-        zIn + (zIn + kDeltaZLum) * (rtRatio_OutIn - 1.f) * (zIn < 0.f ? 1.f : dzDrtScale) + (zpitchIn + zpitchOut);
-    const float zLo = zIn + (zIn - kDeltaZLum) * (rtRatio_OutIn - 1.f) * (zIn > 0.f ? 1.f : dzDrtScale) -
-                      (zpitchIn + zpitchOut);  //slope-correction only on outer end
-
-    //Cut 1 - z compatibility
-    if ((zOut < zLo) || (zOut > zHi))
-      return false;
-
-    float drt_OutIn = (rtOut - rtIn);
-
-    float r3In = alpaka::math::sqrt(acc, zIn * zIn + rtIn * rtIn);
     float drt_InSeg = rtMid - rtIn;
-    float dz_InSeg = zMid - zIn;
-    float dr3_InSeg =
-        alpaka::math::sqrt(acc, rtMid * rtMid + zMid * zMid) - alpaka::math::sqrt(acc, rtIn * rtIn + zIn * zIn);
-
-    float coshEta = dr3_InSeg / drt_InSeg;
-    float dzErr = (zpitchIn + zpitchOut) * (zpitchIn + zpitchOut) * 2.f;
-
-    float thetaMuls2 = (kMulsInGeV * kMulsInGeV) * (0.1f + 0.2f * (rtOut - rtIn) / 50.f) * (r3In / rtIn);
-    float muls2 = thetaMuls2 * 9.f / (ptCut * ptCut) * 16.f;
-    dzErr += muls2 * drt_OutIn * drt_OutIn / 3.f * coshEta * coshEta;
-    dzErr = alpaka::math::sqrt(acc, dzErr);
-
-    // Constructing upper and lower bound
-    const float dzMean = dz_InSeg / drt_InSeg * drt_OutIn;
-    const float zWindow = dzErr / drt_InSeg * drt_OutIn +
-                          (zpitchIn + zpitchOut);  //FIXME for ptCut lower than ~0.8 need to add curv path correction
-    const float zLoPointed = zIn + dzMean * (zIn > 0.f ? 1.f : dzDrtScale) - zWindow;
-    const float zHiPointed = zIn + dzMean * (zIn < 0.f ? 1.f : dzDrtScale) + zWindow;
-
-    // Constructing upper and lower bound
-
-    // Cut #2: Pointed Z (Inner segment two MD points to outer segment inner MD)
-    if ((zOut < zLoPointed) || (zOut > zHiPointed))
-      return false;
 
     // raw betaIn value without any correction, based on the mini-doublet hit positions
     float alpha_InLo = __H2F(segments.dPhiChanges()[innerSegmentIndex]);
@@ -217,7 +408,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
         alpaka::math::asin(acc, alpaka::math::min(acc, (-rt_InSeg + drt_tl_axis) * k2Rinv1GeVf / ptCut, kSinAlphaMax)) +
         (0.02f / drt_InSeg);
 
-    //Cut #3: first beta cut
+    //Beta cut
     return alpaka::math::abs(acc, betaIn) < betaInCut;
   }
 
@@ -238,72 +429,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                 unsigned int innerSegmentIndex,
                                                                 unsigned int outerSegmentIndex,
                                                                 float& betaIn,
-                                                                float& betaInCut) {
-    bool isPSIn = (modules.moduleType()[innerInnerLowerModuleIndex] == PS);
-    bool isPSOut = (modules.moduleType()[outerOuterLowerModuleIndex] == PS);
-
-    float rtIn = mds.anchorRt()[firstMDIndex];
-    float rtMid = mds.anchorRt()[secondMDIndex];
-    rtOut = mds.anchorRt()[thirdMDIndex];
-
-    float zIn = mds.anchorZ()[firstMDIndex];
-    float zMid = mds.anchorZ()[secondMDIndex];
-    zOut = mds.anchorZ()[thirdMDIndex];
-
-    float alpha1GeV_OutLo = alpaka::math::asin(acc, alpaka::math::min(acc, rtOut * k2Rinv1GeVf / ptCut, kSinAlphaMax));
-
-    float dzDrtScale =
-        alpaka::math::tan(acc, alpha1GeV_OutLo) / alpha1GeV_OutLo;  // The track can bend in r-z plane slightly
-    float zpitchIn = (isPSIn ? kPixelPSZpitch : kStrip2SZpitch);
-    float zpitchOut = (isPSOut ? kPixelPSZpitch : kStrip2SZpitch);
-    float zGeom = zpitchIn + zpitchOut;
-
-    // Cut #0: Preliminary (Only here in endcap case)
-    if (zIn * zOut <= 0)
-      return false;
-
-    float dLum = alpaka::math::copysign(acc, kDeltaZLum, zIn);
-    bool isOutSgInnerMDPS = modules.moduleType()[outerOuterLowerModuleIndex] == PS;
-    float rtGeom1 = isOutSgInnerMDPS ? kPixelPSZpitch : kStrip2SZpitch;
-    float zGeom1 = alpaka::math::copysign(acc, zGeom, zIn);
-    float rtLo = rtIn * (1.f + (zOut - zIn - zGeom1) / (zIn + zGeom1 + dLum) / dzDrtScale) -
-                 rtGeom1;  //slope correction only on the lower end
-
-    //Cut #1: rt condition
-    float zInForHi = zIn - zGeom1 - dLum;
-    if (zInForHi * zIn < 0) {
-      zInForHi = alpaka::math::copysign(acc, 0.1f, zIn);
-    }
-    float rtHi = rtIn * (1.f + (zOut - zIn + zGeom1) / zInForHi) + rtGeom1;
-
-    //Cut #2: rt condition
-    if ((rtOut < rtLo) || (rtOut > rtHi))
-      return false;
-
-    float rIn = alpaka::math::sqrt(acc, zIn * zIn + rtIn * rtIn);
-
-    const float drtSDIn = rtMid - rtIn;
-    const float dzSDIn = zMid - zIn;
-    const float dr3SDIn =
-        alpaka::math::sqrt(acc, rtMid * rtMid + zMid * zMid) - alpaka::math::sqrt(acc, rtIn * rtIn + zIn * zIn);
-
-    const float coshEta = dr3SDIn / drtSDIn;  //direction estimate
-    const float dzOutInAbs = alpaka::math::abs(acc, zOut - zIn);
-    const float multDzDr = dzOutInAbs * coshEta / (coshEta * coshEta - 1.f);
-    const float zGeom1_another = kPixelPSZpitch;
-    const float kZ = (zOut - zIn) / dzSDIn;
-    float drtErr =
-        zGeom1_another * zGeom1_another * drtSDIn * drtSDIn / dzSDIn / dzSDIn * (1.f - 2.f * kZ + 2.f * kZ * kZ);
-    const float thetaMuls2 = (kMulsInGeV * kMulsInGeV) * (0.1f + 0.2 * (rtOut - rtIn) / 50.f) * (rIn / rtIn);
-    const float muls2 = thetaMuls2 * 9.f / (ptCut * ptCut) * 16.f;
-    drtErr += muls2 * multDzDr * multDzDr / 3.f * coshEta * coshEta;
-    drtErr = alpaka::math::sqrt(acc, drtErr);
-
-    //Cut #3: rt-z pointed
-
-    if ((kZ < 0) || (rtOut < rtLo) || (rtOut > rtHi))
-      return false;
-
+                                                                float& betaInCut,
+                                                                const float ptCut) {
     float rt_InLo = mds.anchorRt()[firstMDIndex];
     float rt_InOut = mds.anchorRt()[secondMDIndex];
 
@@ -336,7 +463,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     betaInCut = alpaka::math::asin(acc, alpaka::math::min(acc, (-sdIn_dr + dr) * k2Rinv1GeVf / ptCut, kSinAlphaMax)) +
                 (0.02f / sdIn_d);
 
-    //Cut #4: first beta cut
+    //Beta cut
     return alpaka::math::abs(acc, betaInRHmin) < betaInCut;
   }
 
@@ -356,75 +483,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                 unsigned int innerSegmentIndex,
                                                                 unsigned int outerSegmentIndex,
                                                                 float& betaIn,
-                                                                float& betaInCut) {
-    float rtIn = mds.anchorRt()[firstMDIndex];
-    float rtMid = mds.anchorRt()[secondMDIndex];
-    rtOut = mds.anchorRt()[thirdMDIndex];
-
-    float zIn = mds.anchorZ()[firstMDIndex];
-    float zMid = mds.anchorZ()[secondMDIndex];
-    zOut = mds.anchorZ()[thirdMDIndex];
-
-    float alpha1GeV_Out = alpaka::math::asin(acc, alpaka::math::min(acc, rtOut * k2Rinv1GeVf / ptCut, kSinAlphaMax));
-
-    float dzDrtScale =
-        alpaka::math::tan(acc, alpha1GeV_Out) / alpha1GeV_Out;  // The track can bend in r-z plane slightly
-
-    // Cut #0: Preliminary (Only here in endcap case)
-    if (zIn * zOut <= 0)
-      return false;
-
-    float dLum = alpaka::math::copysign(acc, kDeltaZLum, zIn);
-    bool isOutSgOuterMDPS = modules.moduleType()[outerOuterLowerModuleIndex] == PS;
-    bool isInSgInnerMDPS = modules.moduleType()[innerInnerLowerModuleIndex] == PS;
-
-    float rtGeom = (isInSgInnerMDPS and isOutSgOuterMDPS)  ? 2.f * kPixelPSZpitch
-                   : (isInSgInnerMDPS or isOutSgOuterMDPS) ? kPixelPSZpitch + kStrip2SZpitch
-                                                           : 2.f * kStrip2SZpitch;
-
-    float dz = zOut - zIn;
-    const float rtLo = rtIn * (1.f + dz / (zIn + dLum) / dzDrtScale) - rtGeom;  //slope correction only on the lower end
-    const float rtHi = rtIn * (1.f + dz / (zIn - dLum)) + rtGeom;
-
-    //Cut #1: rt condition
-    if ((rtOut < rtLo) || (rtOut > rtHi))
-      return false;
-
-    bool isInSgOuterMDPS = modules.moduleType()[outerOuterLowerModuleIndex] == PS;
-
-    float drtSDIn = rtMid - rtIn;
-    float dzSDIn = zMid - zIn;
-    float dr3SDIn =
-        alpaka::math::sqrt(acc, rtMid * rtMid + zMid * zMid) - alpaka::math::sqrt(acc, rtIn * rtIn + zIn * zIn);
-
-    float coshEta = dr3SDIn / drtSDIn;  //direction estimate
-    float dzOutInAbs = alpaka::math::abs(acc, zOut - zIn);
-    float multDzDr = dzOutInAbs * coshEta / (coshEta * coshEta - 1.f);
-
-    float kZ = (zOut - zIn) / dzSDIn;
-    float thetaMuls2 = (kMulsInGeV * kMulsInGeV) * (0.1f + 0.2f * (rtOut - rtIn) / 50.f);
-
-    float muls2 = thetaMuls2 * 9.f / (ptCut * ptCut) * 16.f;
-
-    float drtErr =
-        alpaka::math::sqrt(acc,
-                           kPixelPSZpitch * kPixelPSZpitch * 2.f / (dzSDIn * dzSDIn) * (dzOutInAbs * dzOutInAbs) +
-                               muls2 * multDzDr * multDzDr / 3.f * coshEta * coshEta);
-
-    float drtMean = drtSDIn * dzOutInAbs / alpaka::math::abs(acc, dzSDIn);
-    float rtWindow = drtErr + rtGeom;
-    float rtLo_point = rtIn + drtMean / dzDrtScale - rtWindow;
-    float rtHi_point = rtIn + drtMean + rtWindow;
-
-    // Cut #3: rt-z pointed
-    // https://github.com/slava77/cms-tkph2-ntuple/blob/superDoubletLinked-91X-noMock/doubletAnalysis.C#L3765
-
-    if (isInSgInnerMDPS and isInSgOuterMDPS)  // If both PS then we can point
-    {
-      if ((kZ < 0) || (rtOut < rtLo_point) || (rtOut > rtHi_point))
-        return false;
-    }
-
+                                                                float& betaInCut,
+                                                                const float ptCut) {
     float rt_InLo = mds.anchorRt()[firstMDIndex];
     float rt_InOut = mds.anchorRt()[secondMDIndex];
     float sdIn_alpha = __H2F(segments.dPhiChanges()[innerSegmentIndex]);
@@ -457,7 +517,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     betaInCut = alpaka::math::asin(acc, alpaka::math::min(acc, (-sdIn_dr + dr) * k2Rinv1GeVf / ptCut, kSinAlphaMax)) +
                 (0.02f / sdIn_d);
 
-    //Cut #4: first beta cut
+    //Beta cut
     return alpaka::math::abs(acc, betaInRHmin) < betaInCut;
   }
 
@@ -478,7 +538,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                              unsigned int innerSegmentIndex,
                                                              unsigned int outerSegmentIndex,
                                                              float& betaIn,
-                                                             float& betaInCut) {
+                                                             float& betaInCut,
+                                                             const float ptCut) {
     short innerInnerLowerModuleSubdet = modules.subdets()[innerInnerLowerModuleIndex];
     short middleLowerModuleSubdet = modules.subdets()[middleLowerModuleIndex];
     short outerOuterLowerModuleSubdet = modules.subdets()[outerOuterLowerModuleIndex];
@@ -499,7 +560,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                        rtOut,
                                        innerSegmentIndex,
                                        betaIn,
-                                       betaInCut);
+                                       betaInCut,
+                                       ptCut);
     } else if (innerInnerLowerModuleSubdet == Barrel and middleLowerModuleSubdet == Barrel and
                outerOuterLowerModuleSubdet == Endcap) {
       return passPointingConstraintBBE(acc,
@@ -518,7 +580,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                        innerSegmentIndex,
                                        outerSegmentIndex,
                                        betaIn,
-                                       betaInCut);
+                                       betaInCut,
+                                       ptCut);
     } else if (innerInnerLowerModuleSubdet == Barrel and middleLowerModuleSubdet == Endcap and
                outerOuterLowerModuleSubdet == Endcap) {
       return passPointingConstraintBBE(acc,
@@ -537,7 +600,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                        innerSegmentIndex,
                                        outerSegmentIndex,
                                        betaIn,
-                                       betaInCut);
+                                       betaInCut,
+                                       ptCut);
 
     }
 
@@ -558,7 +622,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                        innerSegmentIndex,
                                        outerSegmentIndex,
                                        betaIn,
-                                       betaInCut);
+                                       betaInCut,
+                                       ptCut);
     }
     return false;  // failsafe
   }
@@ -612,7 +677,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                                    float& betaInCut,
                                                                    float& circleRadius,
                                                                    float& circleCenterX,
-                                                                   float& circleCenterY) {
+                                                                   float& circleCenterY,
+                                                                   const float ptCut) {
     //this cut reduces the number of candidates by a factor of 4, i.e., 3 out of 4 warps can end right here!
     if (segments.mdIndices()[innerSegmentIndex][1] != segments.mdIndices()[outerSegmentIndex][0])
       return false;
@@ -621,6 +687,15 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     unsigned int secondMDIndex = segments.mdIndices()[outerSegmentIndex][0];
     unsigned int thirdMDIndex = segments.mdIndices()[outerSegmentIndex][1];
 
+    float x1 = mds.anchorX()[firstMDIndex];
+    float x2 = mds.anchorX()[secondMDIndex];
+    float x3 = mds.anchorX()[thirdMDIndex];
+    float y1 = mds.anchorY()[firstMDIndex];
+    float y2 = mds.anchorY()[secondMDIndex];
+    float y3 = mds.anchorY()[thirdMDIndex];
+
+    circleRadius = computeRadiusFromThreeAnchorHits(acc, x1, y1, x2, y2, x3, y3, circleCenterX, circleCenterY);
+
     if (not passRZConstraint(acc,
                              modules,
                              mds,
@@ -629,8 +704,12 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                              outerOuterLowerModuleIndex,
                              firstMDIndex,
                              secondMDIndex,
-                             thirdMDIndex))
+                             thirdMDIndex,
+                             circleRadius,
+                             circleCenterX,
+                             circleCenterY))
       return false;
+
     if (not passPointingConstraint(acc,
                                    modules,
                                    mds,
@@ -647,17 +726,10 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                    innerSegmentIndex,
                                    outerSegmentIndex,
                                    betaIn,
-                                   betaInCut))
+                                   betaInCut,
+                                   ptCut))
       return false;
 
-    float x1 = mds.anchorX()[firstMDIndex];
-    float x2 = mds.anchorX()[secondMDIndex];
-    float x3 = mds.anchorX()[thirdMDIndex];
-    float y1 = mds.anchorY()[firstMDIndex];
-    float y2 = mds.anchorY()[secondMDIndex];
-    float y3 = mds.anchorY()[thirdMDIndex];
-
-    circleRadius = computeRadiusFromThreeAnchorHits(acc, x1, y1, x2, y2, x3, y3, circleCenterX, circleCenterY);
     return true;
   }
 
@@ -672,7 +744,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                   TripletsOccupancy tripletsOccupancy,
                                   ObjectRangesConst ranges,
                                   uint16_t* index_gpu,
-                                  uint16_t nonZeroModules) const {
+                                  uint16_t nonZeroModules,
+                                  const float ptCut) const {
       auto const globalThreadIdx = alpaka::getIdx<alpaka::Grid, alpaka::Threads>(acc);
       auto const gridThreadExtent = alpaka::getWorkDiv<alpaka::Grid, alpaka::Threads>(acc);
 
@@ -719,7 +792,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
                                                         betaInCut,
                                                         circleRadius,
                                                         circleCenterX,
-                                                        circleCenterY);
+                                                        circleCenterY,
+                                                        ptCut);
 
             if (success) {
               unsigned int totOccupancyTriplets =
@@ -730,7 +804,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
               if (static_cast<int>(totOccupancyTriplets) >=
                   ranges.tripletModuleOccupancy()[innerInnerLowerModuleIndex]) {
 #ifdef WARNINGS
-                printf("Triplet excess alert! Module index = %d\n", innerInnerLowerModuleIndex);
+                printf("Triplet excess alert! Module index = %d, Occupancy = %d\n",
+                       innerInnerLowerModuleIndex,
+                       totOccupancyTriplets);
 #endif
               } else {
                 unsigned int tripletModuleIndex = alpaka::atomicAdd(
@@ -769,7 +845,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
     ALPAKA_FN_ACC void operator()(TAcc const& acc,
                                   ModulesConst modules,
                                   ObjectRanges ranges,
-                                  SegmentsOccupancyConst segmentsOccupancy) const {
+                                  SegmentsOccupancyConst segmentsOccupancy,
+                                  const float ptCut) const {
       // implementation is 1D with a single block
       static_assert(std::is_same_v<TAcc, ALPAKA_ACCELERATOR_NAMESPACE::Acc1D>, "Should be Acc1D");
       ALPAKA_ASSERT_ACC((alpaka::getWorkDiv<alpaka::Grid, alpaka::Blocks>(acc)[0] == 1));
@@ -784,6 +861,25 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
       }
       alpaka::syncBlockThreads(acc);
 
+      // Occupancy matrix for 0.8 GeV pT Cut
+      constexpr int p08_occupancy_matrix[4][4] = {
+          {543, 235, 88, 46},  // category 0
+          {755, 347, 0, 0},    // category 1
+          {0, 0, 0, 0},        // category 2
+          {0, 38, 46, 39}      // category 3
+      };
+
+      // Occupancy matrix for 0.6 GeV pT Cut, 99.9%
+      constexpr int p06_occupancy_matrix[4][4] = {
+          {1146, 544, 216, 83},  // category 0
+          {1032, 275, 0, 0},     // category 1
+          {0, 0, 0, 0},          // category 2
+          {0, 115, 110, 76}      // category 3
+      };
+
+      // Select the appropriate occupancy matrix based on ptCut
+      const auto& occupancy_matrix = (ptCut < 0.8f) ? p06_occupancy_matrix : p08_occupancy_matrix;
+
       for (uint16_t i = globalThreadIdx[0]; i < modules.nLowerModules(); i += gridThreadExtent[0]) {
         if (segmentsOccupancy.nSegments()[i] == 0) {
           ranges.tripletModuleIndices()[i] = nTotalTriplets;
@@ -796,63 +892,18 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst {
         short module_subdets = modules.subdets()[i];
         float module_eta = alpaka::math::abs(acc, modules.eta()[i]);
 
-        int category_number;
-        if (module_layers <= 3 && module_subdets == 5)
-          category_number = 0;
-        else if (module_layers >= 4 && module_subdets == 5)
-          category_number = 1;
-        else if (module_layers <= 2 && module_subdets == 4 && module_rings >= 11)
-          category_number = 2;
-        else if (module_layers >= 3 && module_subdets == 4 && module_rings >= 8)
-          category_number = 2;
-        else if (module_layers <= 2 && module_subdets == 4 && module_rings <= 10)
-          category_number = 3;
-        else if (module_layers >= 3 && module_subdets == 4 && module_rings <= 7)
-          category_number = 3;
-        else
-          category_number = -1;
-
-        int eta_number;
-        if (module_eta < 0.75f)
-          eta_number = 0;
-        else if (module_eta < 1.5f)
-          eta_number = 1;
-        else if (module_eta < 2.25f)
-          eta_number = 2;
-        else if (module_eta < 3.0f)
-          eta_number = 3;
-        else
-          eta_number = -1;
-
-        int occupancy;
-        if (category_number == 0 && eta_number == 0)
-          occupancy = 543;
-        else if (category_number == 0 && eta_number == 1)
-          occupancy = 235;
-        else if (category_number == 0 && eta_number == 2)
-          occupancy = 88;
-        else if (category_number == 0 && eta_number == 3)
-          occupancy = 46;
-        else if (category_number == 1 && eta_number == 0)
-          occupancy = 755;
-        else if (category_number == 1 && eta_number == 1)
-          occupancy = 347;
-        else if (category_number == 2 && eta_number == 1)
-          occupancy = 0;
-        else if (category_number == 2 && eta_number == 2)
-          occupancy = 0;
-        else if (category_number == 3 && eta_number == 1)
-          occupancy = 38;
-        else if (category_number == 3 && eta_number == 2)
-          occupancy = 46;
-        else if (category_number == 3 && eta_number == 3)
-          occupancy = 39;
-        else {
-          occupancy = 0;
+        int category_number = getCategoryNumber(module_layers, module_subdets, module_rings);
+        int eta_number = getEtaBin(module_eta);
+
+        int occupancy = 0;
+        if (category_number != -1 && eta_number != -1) {
+          occupancy = occupancy_matrix[category_number][eta_number];
+        }
 #ifdef WARNINGS
+        else {
           printf("Unhandled case in createTripletArrayRanges! Module index = %i\n", i);
-#endif
         }
+#endif
 
         ranges.tripletModuleOccupancy()[i] = occupancy;
         unsigned int nTotT = alpaka::atomicAdd(acc, &nTotalTriplets, occupancy, alpaka::hierarchy::Threads{});
diff --git a/RecoTracker/LSTCore/standalone/.gitignore b/RecoTracker/LSTCore/standalone/.gitignore
index 29e86cb6b932a..3d27afd0c4469 100644
--- a/RecoTracker/LSTCore/standalone/.gitignore
+++ b/RecoTracker/LSTCore/standalone/.gitignore
@@ -9,6 +9,7 @@ plots_*/
 scripts/moduleconnection*.txt
 *.root
 .make.log*
+performance*
 bin/doAnalysis
 bin/lst
 bin/lst_cuda
diff --git a/RecoTracker/LSTCore/standalone/LST/Makefile b/RecoTracker/LSTCore/standalone/LST/Makefile
index ee6f82ecccde1..3790a6031b0c8 100644
--- a/RecoTracker/LSTCore/standalone/LST/Makefile
+++ b/RecoTracker/LSTCore/standalone/LST/Makefile
@@ -67,7 +67,7 @@ CMSSW_WERRORS_CPU    = -Werror=pointer-arith -Werror=overlength-strings -Werror=
                        -Wno-unused-parameter -Wno-unused-local-typedefs -Wno-attributes
 CMSSW_WERRORS_CUDA   = $(patsubst %,-Xcompiler %,$(CMSSW_WERRORS_CPU))
 CMSSW_WERRORS_ROCM   = $(CMSSW_WERRORS_CPU)
-T5CUTFLAGS           = $(T5DNNFLAG) $(T5RZCHI2FLAG) $(T5RPHICHI2FLAG)
+T5CUTFLAGS           = $(T5DNNFLAG) $(T5RZCHI2FLAG)
 
 LD_CPU               = g++
 SOFLAGS_CPU          = -g -shared -fPIC
@@ -88,31 +88,31 @@ CUTVALUEFLAG =
 CUTVALUEFLAG_FLAGS = -DCUT_VALUE_DEBUG
 
 %_cpu.o: ../../src/alpaka/%.dev.cc
-	$(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@
+	$(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@
 
 %_cuda.o: ../../src/alpaka/%.dev.cc
-	$(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@
+	$(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@
 
 %_rocm.o: ../../src/alpaka/%.dev.cc
-	$(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@
+	$(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@
 
 %_cpu.o: ../../src/alpaka/%.cc
-	$(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@
+	$(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@
 
 %_cuda.o: ../../src/alpaka/%.cc
-	$(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@
+	$(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@
 
 %_rocm.o: ../../src/alpaka/%.cc
-	$(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@
+	$(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@
 
 %_cpu.o: ../../src/%.cc
-	$(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@
+	$(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@
 
 %_cuda.o: ../../src/%.cc
-	$(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@
+	$(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@
 
 %_rocm.o: ../../src/%.cc
-	$(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@
+	$(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@
 
 $(LIB_CPU): $(CCOBJECTS_CPU) $(LSTOBJECTS_CPU)
 	$(LD_CPU) $(SOFLAGS_CPU) $^ -o $@
diff --git a/RecoTracker/LSTCore/standalone/Makefile b/RecoTracker/LSTCore/standalone/Makefile
index b98df31df1b5e..18ec73db8d975 100644
--- a/RecoTracker/LSTCore/standalone/Makefile
+++ b/RecoTracker/LSTCore/standalone/Makefile
@@ -25,7 +25,6 @@ ALPAKA_CUDA = -DALPAKA_ACC_GPU_CUDA_ENABLED -DALPAKA_HOST_ONLY -DALPAKA_DISABLE_
 ALPAKA_ROCM = -DALPAKA_ACC_GPU_HIP_ENABLED -DALPAKA_HOST_ONLY -DALPAKA_DISABLE_VENDOR_RNG -D__HIP_PLATFORM_HCC__ -D__HIP_PLATFORM_AMD__ -DALPAKA_DEFAULT_HOST_MEMORY_ALIGNMENT=128
 EXTRAFLAGS  = -ITMultiDrawTreePlayer -Wunused-variable -lTMVA -lEG -lGenVector -lXMLIO -lMLP -lTreePlayer -fopenmp
 DOQUINTUPLET =
-PTCUTFLAG    =
 CUTVALUEFLAG =
 CUTVALUEFLAG_FLAGS = -DCUT_VALUE_DEBUG
 
@@ -47,20 +46,20 @@ cutvalue_primitive: rooutil efficiency $(EXES)
 
 bin/lst_cpu: LSTLIB=-llst_cpu
 bin/lst_cpu: bin/lst_cpu.o $(OBJECTS_CPU)
-	$(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CPU) -o $@
+	$(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CPU) -o $@
 bin/lst_cuda: LSTLIB=-llst_cuda
 bin/lst_cuda: bin/lst_cuda.o $(OBJECTS_CUDA)
-	$(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CUDA) $(LDFLAGS_CUDA) -o $@
+	$(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CUDA) $(LDFLAGS_CUDA) -o $@
 bin/lst_rocm: LSTLIB=-llst_rocm
 bin/lst_rocm: bin/lst_rocm.o $(OBJECTS_ROCM)
-	$(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_ROCM) $(LDFLAGS_ROCM) -o $@
+	$(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_ROCM) $(LDFLAGS_ROCM) -o $@
 
 %_cpu.o: %.cc rooutil
-	$(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CPU) $< -c -o $@
+	$(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CPU) $< -c -o $@
 %_cuda.o: %.cc rooutil
-	$(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CUDA) $(CUDAINCLUDE) $< -c -o $@
+	$(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CUDA) $(CUDAINCLUDE) $< -c -o $@
 %_rocm.o: %.cc rooutil
-	$(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_ROCM) $(ROCMINCLUDE) $< -c -o $@
+	$(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_ROCM) $(ROCMINCLUDE) $< -c -o $@
 
 rooutil:
 	$(MAKE) -C code/rooutil/
diff --git a/RecoTracker/LSTCore/standalone/README.md b/RecoTracker/LSTCore/standalone/README.md
index 02fbef943f697..ed68ea6797f8e 100644
--- a/RecoTracker/LSTCore/standalone/README.md
+++ b/RecoTracker/LSTCore/standalone/README.md
@@ -1,53 +1,93 @@
-# TrackLooper
+# How to set up standalone LST
 
-
-## Quick Start
-
-
-### Setting up LSTPerformanceWeb (only for lnx7188 and lnx4555)
+## Setting up LSTPerformanceWeb (only for lnx7188 and lnx4555)
 
 For lnx7188 and lnx4555 this needs to be done once
 
     cd /cdat/tem/${USER}/
     git clone git@github.com:SegmentLinking/LSTPerformanceWeb.git
 
-### Setting up container (only for lnx7188)
+## Setting up container (only for lnx7188)
 
 For lnx7188 this needs to be done before compiling or running the code:
 
     singularity shell --nv --bind /mnt/data1:/data --bind /data2/segmentlinking/ --bind /opt --bind /nfs --bind /mnt --bind /usr/local/cuda/bin/ --bind /cvmfs  /cvmfs/unpacked.cern.ch/registry.hub.docker.com/cmssw/el8:x86_64
 
-### Setting up the code
+## Setting up LST
+
+There are two way to set up LST as a standalone, either by setting up a full CMSSW area, which provides a unified setup for standalone and CMSSW tests, or by `sparse-checkout` only the relevant package and using them independent of CMSSW. A CVMFS-less setup is also provided for the second option.
+
+### Setting up LST within CMSSW (preferred option)
+
+```bash
+CMSSW_VERSION=CMSSW_14_2_0_pre4 # Change with latest/preferred CMSSW version
+cmsrel ${CMSSW_VERSION}
+cd ${CMSSW_VERSION}/src/
+cmsenv
+git cms-init
+# If necessary, add the remote git@github.com:SegmentLinking/cmssw.git
+# and checkout a development/feature branch
+git cms-addpkg RecoTracker/LST RecoTracker/LSTCore
+# If modifying some dependencies, run `git cms-checkdeps -a -A`
+scram b -j 12
+cd RecoTracker/LSTCore/standalone
+```
+
+The data files for LST will be fetched from CVMFS. However, if new data files are needed, the need to be manually placed (under `$CMSSW_BASE/external/$SCRAM_ARCH/data/RecoTracker/LSTCore/data/`). This is done by running:
 
-    git clone git@github.com:SegmentLinking/TrackLooper.git
-    cd TrackLooper/
-    # Source one of the commands below, depending on the site
-    source setup.sh # if on UCSD or Cornell
-    source setup_hpg.sh # if on Florida
+```bash
+mkdir -p $CMSSW_BASE/external/$SCRAM_ARCH/data/RecoTracker/LSTCore/
+cd $CMSSW_BASE/external/$SCRAM_ARCH/data/RecoTracker/LSTCore/
+git clone git@github.com:cms-data/RecoTracker-LSTCore.git data
+<modify the files or checkout a different branch>
+cd -
+```
 
-### Running the code
+### Setting up LST outside of CMSSW
 
-    sdl_make_tracklooper -mc
-    sdl_<backend> -i PU200 -o LSTNtuple.root
+For this setup, dependencies are still provided from CMSSW through CVMFS but no CMSSW area is setup. This is done by running the following commands.
+
+``` bash
+LST_BRANCH=master # Change to the development branch
+git clone --filter=blob:none --no-checkout --depth 1 --sparse --branch ${LST_BRANCH} https://github.com/SegmentLinking/cmssw.git TrackLooper
+cd TrackLooper
+git sparse-checkout add RecoTracker/LSTCore
+git checkout
+cd RecoTracker/LSTCore/standalone/
+```
+
+As in the sectino above, the data files are fetched from CVMFS, but they can also be copied manually under `RecoTracker/LSTCore/data/`.
+
+
+## Running the code
+
+Each time the standalone version of LST is to be used, the following command should be run from the `RecoTracker/LSTCore/standalone` directory:
+```bash
+source setup.sh
+```
+
+For running the code:
+
+    lst_make_tracklooper -m
+    lst_<backend> -i PU200 -o LSTNtuple.root
     createPerfNumDenHists -i LSTNtuple.root -o LSTNumDen.root
-    lst_plot_performance.py LSTNumDen.root -t "myTag"
-    # python3 efficiency/python/lst_plot_performance.py LSTNumDen.root -t "myTag" # if you are on cgpu-1 or Cornell
+    lst_plot_performance.py LSTNumDen.root -t "myTag" # or
+    python3 efficiency/python/lst_plot_performance.py LSTNumDen.root -t "myTag" # if you are on cgpu-1 or Cornell
 
 The above can be even simplified
 
-    sdl_run -f -mc -s PU200 -n -1 -t myTag
+    lst_run -f -m -s PU200 -n -1 -t myTag
 
 The `-f` flag can be omitted when the code has already been compiled. If multiple backends were compiled, then the `-b` flag can be used to specify a backend. For example
 
-    sdl_run -b cpu -s PU200 -n -1 -t myTag
+    lst_run -b cpu -s PU200 -n -1 -t myTag
 
-## Command explanations
+### Command explanations
 
 Compile the code with option flags. If none of `C,G,R,A` are used, then it defaults to compiling for CUDA and CPU.
 
-    sdl_make_tracklooper -mc
+    lst_make_tracklooper -m
     -m: make clean binaries
-    -c: run with the cmssw caching allocator
     -C: compile CPU backend
     -G: compile CUDA backend
     -R: compile ROCm backend
@@ -56,7 +96,7 @@ Compile the code with option flags. If none of `C,G,R,A` are used, then it defau
 
 Run the code
  
-    sdl_<backend> -n <nevents> -v <verbose> -w <writeout> -s <streams> -i <dataset> -o <output>
+    lst_<backend> -n <nevents> -v <verbose> -w <writeout> -s <streams> -i <dataset> -o <output>
 
     -i: PU200; muonGun, etc
     -n: number of events; default: all
@@ -106,186 +146,40 @@ Comparing two different runs
         -t "mywork" \
         --compare
 
-## CMSSW Integration
-This is the a complete set of instruction on how the TrackLooper code
-can be linked as an external tool in CMSSW:
-
-### Build TrackLooper
-```bash
-git clone git@github.com:SegmentLinking/TrackLooper.git
-cd TrackLooper/
-# Source one of the commands below, depending on the site
-source setup.sh # if on UCSD or Cornell
-source setup_hpg.sh # if on Florida
-sdl_make_tracklooper -mc
-cd ..
-```
-
-### Set up `TrackLooper` as an external
-```bash
-mkdir workingFolder # Create the folder you will be working in
-cd workingFolder
-cmsrel CMSSW_14_1_0_pre3
-cd CMSSW_14_1_0_pre3/src
-cmsenv
-git cms-init
-git remote add SegLink git@github.com:SegmentLinking/cmssw.git
-git fetch SegLink CMSSW_14_1_0_pre3_LST_X
-git cms-addpkg RecoTracker Configuration
-git checkout CMSSW_14_1_0_pre3_LST_X
-#To include both the CPU library and GPU library into CMSSW, create 3 xml files (headers file has no library).
-#Before writing the following xml file, check that libsdl_cpu.so and libsdl_gpu.so can be found under the ../../../TrackLooper/SDL/ folder.
-cat <<EOF >lst_headers.xml
-<tool name="lst_headers" version="1.0">
-  <client>
-    <environment name="LSTBASE" default="$PWD/../../../TrackLooper"/>
-    <environment name="INCLUDE" default="\$LSTBASE"/>
-  </client>
-  <runtime name="LST_BASE" value="\$LSTBASE"/>
-</tool>
-EOF
-cat <<EOF >lst_cpu.xml
-<tool name="lst_cpu" version="1.0">
-  <client>
-    <environment name="LSTBASE" default="$PWD/../../../TrackLooper"/>
-    <environment name="LIBDIR" default="\$LSTBASE/SDL"/>
-    <environment name="INCLUDE" default="\$LSTBASE"/>
-  </client>
-  <runtime name="LST_BASE" value="\$LSTBASE"/>
-  <lib name="sdl_cpu"/>
-</tool>
-EOF
-cat <<EOF >lst_cuda.xml
-<tool name="lst_cuda" version="1.0">
-  <client>
-    <environment name="LSTBASE" default="$PWD/../../../TrackLooper"/>
-    <environment name="LIBDIR" default="\$LSTBASE/SDL"/>
-    <environment name="INCLUDE" default="\$LSTBASE"/>
-  </client>
-  <runtime name="LST_BASE" value="\$LSTBASE"/>
-  <lib name="sdl_cuda"/>
-</tool>
-EOF
-scram setup lst_headers.xml
-scram setup lst_cpu.xml
-scram setup lst_cuda.xml
-cmsenv
-git cms-checkdeps -a -A
-scram b -j 12
-```
-
-### Run the LST reconstruction in CMSSW
-A simple test configuration of the LST reconstruction can be run with the command:
-```bash
-cmsRun RecoTracker/LST/test/LSTAlpakaTester.py
-```
+# How to set up CMSSW with LST
 
-For a more complete workflow, one can run a modified version of the 21034.1 workflow.
-To get the commands of this workflow, one can run:
-```bash
-runTheMatrix.py -w upgrade -n -e -l 21034.1
-```
+## Setting up the area
 
-For convenience, the workflow has been run for 100 events and the output is stored here:
-```bash
-/data2/segmentlinking/CMSSW_14_1_0_pre0/step2_21034.1_100Events.root
-```
+Follow the instructions in the ["Setting up LST within CMSSW" section](#setting-up-lst-within-cmssw-preferred-option).
 
-For enabling the LST reconstruction in the CMSSW tracking workflow, a modified step3 needs to be run.
-This is based on the step3 command of the 21034.1 workflow with the following changes:
-   - Remove the `--pileup_input` and `--pileup` flags.
-   - The number of threads and streams for the job can be optionally controlled by the `--nThreads` and `--nStreams` command line options respectively (`1` ends up being the actual default value for both, and more info can be found by running `cmsDriver.py --help`).
-   - Add at the end of the command: `--procModifiers gpu,trackingLST,trackingIters01 --no_exec`
-
-Run the command and modify the output configuration file with the following:
-   - If want to run a cpu version, remove the ```gpu``` in the line defining the `process` object:
-     ```python
-     process = cms.Process('RECO',...,gpu,...)
-     ```
-   - Add the following lines below the part where the import of the standard configurations happens:
-     ```python
-     process.load('Configuration.StandardSequences.Accelerators_cff')
-     process.load("HeterogeneousCore.AlpakaCore.ProcessAcceleratorAlpaka_cfi")
-     ```
-   - Modify the input and output file names accordingly, as well as the number of events.
-
-Then, run the configuration file with `cmsRun`.
-
-To get the DQM files, one would have to run step4 of the 21034.1 workflow with the following modifications:
-   - Add `--no_exec` to the end of command and then run it.
-   - Modify the output configuration file by changing the input file (the one containing `inDQM` from the previous step) and number of events accordingly.
+## Run the LST reconstruction in CMSSW (read to the end, before running)
 
-Running the configuration file with `cmsRun`, the output file will have a name starting with `DQM`. The name is the same every time this step runs,
-so it is good practice to rename the file, e.g. to `tracking_Iters01LST.root`.
-The MTV plots can be produced with the command:
-```bash
-makeTrackValidationPlots.py --extended tracking_Iters01LST.root
-```
-Comparison plots can be made by including multiple ROOT files as arguments.
+Two complete workflows have been implemented within CMSSW to run a two-iteration, tracking-only reconstruction with LST:
+ - 24834.703 (CPU)
+ - 24834.704 (GPU)
 
-**Note:** In case one wants to run step2 as well, similar modifications as in step4 (`--no_exec` flag and input file/number of events) need to be applied. Moreover, the PU files have better be modified to point to local ones. This can be done by inserting a dummy file when running the command (set the argument of the `--pileup_input` flag to `file:file.root`), and then change the PU input files in the configuration to the following line (by means of replacing the corresponding line in the configuration):
-```python
-process.mix.input.fileNames = cms.untracked.vstring(['file:/data2/segmentlinking/PUSamplesForCMSSW1263/CMSSW_12_3_0_pre5/RelValMinBias_14TeV/GEN-SIM/123X_mcRun4_realistic_v4_2026D88noPU-v1/066fc95d-1cef-4469-9e08-3913973cd4ce.root', 'file:/data2/segmentlinking/PUSamplesForCMSSW1263/CMSSW_12_3_0_pre5/RelValMinBias_14TeV/GEN-SIM/123X_mcRun4_realistic_v4_2026D88noPU-v1/07928a25-231b-450d-9d17-e20e751323a1.root', 'file:/data2/segmentlinking/PUSamplesForCMSSW1263/CMSSW_12_3_0_pre5/RelValMinBias_14TeV/GEN-SIM/123X_mcRun4_realistic_v4_2026D88noPU-v1/26bd8fb0-575e-4201-b657-94cdcb633045.root', 'file:/data2/segmentlinking/PUSamplesForCMSSW1263/CMSSW_12_3_0_pre5/RelValMinBias_14TeV/GEN-SIM/123X_mcRun4_realistic_v4_2026D88noPU-v1/4206a9c5-44c2-45a5-aab2-1a8a6043a08a.root', 'file:/data2/segmentlinking/PUSamplesForCMSSW1263/CMSSW_12_3_0_pre5/RelValMinBias_14TeV/GEN-SIM/123X_mcRun4_realistic_v4_2026D88noPU-v1/55a372bf-a234-4111-8ce0-ead6157a1810.root', 'file:/data2/segmentlinking/PUSamplesForCMSSW1263/CMSSW_12_3_0_pre5/RelValMinBias_14TeV/GEN-SIM/123X_mcRun4_realistic_v4_2026D88noPU-v1/59ad346c-f405-4288-96d7-795f81c43fe8.root', 'file:/data2/segmentlinking/PUSamplesForCMSSW1263/CMSSW_12_3_0_pre5/RelValMinBias_14TeV/GEN-SIM/123X_mcRun4_realistic_v4_2026D88noPU-v1/7280f5ec-b71d-4579-a730-7ce2de0ff906.root', 'file:/data2/segmentlinking/PUSamplesForCMSSW1263/CMSSW_12_3_0_pre5/RelValMinBias_14TeV/GEN-SIM/123X_mcRun4_realistic_v4_2026D88noPU-v1/b93adc85-715f-477a-afc9-65f3241933ee.root', 'file:/data2/segmentlinking/PUSamplesForCMSSW1263/CMSSW_12_3_0_pre5/RelValMinBias_14TeV/GEN-SIM/123X_mcRun4_realistic_v4_2026D88noPU-v1/c7a0aa46-f55c-4b01-977f-34a397b71fba.root', 'file:/data2/segmentlinking/PUSamplesForCMSSW1263/CMSSW_12_3_0_pre5/RelValMinBias_14TeV/GEN-SIM/123X_mcRun4_realistic_v4_2026D88noPU-v1/e77fa467-97cb-4943-884f-6965b4eb0390.root'])
-```
+We will use the second one in the example below. To get the commands of this workflow, one can run:
 
-### Inclusion of LST in other CMSSW packages
-Including the line
-```
-<use name="lst"/>
-```
-in the relevant package `BuildFile.xml` allows for
-including our headers in the code of that package.
+    runTheMatrix.py -w upgrade -n -e -l 24834.704
 
-## Running LST in a CVMFS-less setup
+For convenience, the workflow has been run for 100 events and the output is stored here:
 
-The setup scripts included in this repository assume that the [CernVM File System (CVMFS)](https://cernvm.cern.ch/fs/) is installed. This provides a convenient way to fetch the required dependencies, but it is not necessary to run LST in standalone mode. Here, we briefly describe how to build and run it when CVMFS is not available.
+    /data2/segmentlinking/step2_29834.1_100Events.root
 
-The necessary dependencies are CUDA, ROOT, the Boost libraries, Alpaka, and some CMSSW headers. CUDA, ROOT, and Boost, are fairly standard libraries and are available from multiple package managers. For the remaining necessary headers you will need to clone the [Alpaka](https://github.com/alpaka-group/alpaka) and [CMSSW](https://github.com/cms-sw/cmssw) repositories. The Alpaka repository is reasonably sized, but the CMSSW one extremely large, especially considering that we only need a tiny fraction of its files to build LST. We can get only the Alpaka interface headers from CMSSW by running the following commands.
+The input files in each step may need to be properly adjusted to match the ones produced by the previous step/provided externally, hence it is better to run the commands with the `--no_exec` option included.
 
-``` bash
-git clone --filter=blob:none --no-checkout --depth 1 --sparse --branch CMSSW_14_1_X https://github.com/cms-sw/cmssw.git
-cd cmssw
-git sparse-checkout add HeterogeneousCore/AlpakaInterface
-git checkout
-```
+Running the configuration file with `cmsRun`, the output file will have a name starting with `DQM`. The name is the same every time this step runs,
+so it is good practice to rename the file, e.g. to `step4_24834.704.root`.
+The MTV plots can be produced with the command:
 
-Then all that is left to do is set some environment variables. We give an example of how to do this in lnx7188/cgpu-1.
+    makeTrackValidationPlots.py --extended step4_24834.704.root
 
-```bash
-# These two lines are only needed to set the right version of gcc and nvcc. They are not needed for standard installations.
-export PATH=/cvmfs/cms.cern.ch/el8_amd64_gcc12/external/gcc/12.3.1-40d504be6370b5a30e3947a6e575ca28/bin:/cvmfs/cms.cern.ch/el8_amd64_gcc12/cms/cmssw/CMSSW_14_1_0_pre3/external/el8_amd64_gcc12/bin:$PATH
-export LD_LIBRARY_PATH=/cvmfs/cms.cern.ch/el8_amd64_gcc12/cms/cmssw/CMSSW_14_1_0_pre3/biglib/el8_amd64_gcc12:/cvmfs/cms.cern.ch/el8_amd64_gcc12/cms/cmssw/CMSSW_14_1_0_pre3/lib/el8_amd64_gcc12:/cvmfs/cms.cern.ch/el8_amd64_gcc12/cms/cmssw/CMSSW_14_1_0_pre3/external/el8_amd64_gcc12/lib:/cvmfs/cms.cern.ch/el8_amd64_gcc12/external/gcc/12.3.1-40d504be6370b5a30e3947a6e575ca28/lib64:/cvmfs/cms.cern.ch/el8_amd64_gcc12/external/gcc/12.3.1-40d504be6370b5a30e3947a6e575ca28/lib:$LD_LIBRARY_PATH
-
-# These are the lines that you need to manually change for a CVMFS-less setup.
-# In this example we use cvmfs paths since that is where the dependencies are in lnx7188/cgpu1, but they can point to local directories.
-export BOOST_ROOT=/cvmfs/cms.cern.ch/el8_amd64_gcc12/external/boost/1.80.0-60a217837b5db1cff00c7d88ec42f53a
-export ALPAKA_ROOT=/cvmfs/cms.cern.ch/el8_amd64_gcc12/external/alpaka/1.1.0-7d0324257db47fde2d27987e7ff98fb4
-export CUDA_HOME=/cvmfs/cms.cern.ch/el8_amd64_gcc12/external/cuda/12.4.1-06cde0cd9f95a73a1ea05c8535f60bde
-export ROOT_ROOT=/cvmfs/cms.cern.ch/el8_amd64_gcc12/lcg/root/6.30.07-21947a33e64ceb827a089697ad72e468
-export CMSSW_BASE=/cvmfs/cms.cern.ch/el8_amd64_gcc12/cms/cmssw/CMSSW_14_1_0_pre3
-
-# These lines are needed to account for some extra environment variables that are exported in the setup script.
-export LD_LIBRARY_PATH=$PWD/SDL/cuda:$PWD/SDL/cpu:$PWD:$LD_LIBRARY_PATH
-export PATH=$PWD/bin:$PATH
-export PATH=$PWD/efficiency/bin:$PATH
-export PATH=$PWD/efficiency/python:$PATH
-export TRACKLOOPERDIR=$PWD
-export TRACKINGNTUPLEDIR=/data2/segmentlinking/CMSSW_12_2_0_pre2/
-export LSTOUTPUTDIR=.
-source $PWD/code/rooutil/thisrooutil.sh
-
-# After this, you can compile and run LST as usual.
-sdl_run -f -mc -s PU200 -n -1 -t myTag
-```
+Comparison plots can be made by including multiple ROOT files as arguments.
 
 ## Code formatting and checking
 
-The makefile in the `SDL` directory includes phony targets to run `clang-format` and `clang-tidy` on the code using the formatting and checks used in CMSSW. The following are the available commands.
+Using the first setup option above, it is prefered to run the checks provided by CMSSW using the following commands.
 
-- `make format`
-  Formats the code in the `SDL` directory using `clang-format` following the rules specified in `.clang-format`.
-- `make check`
-  Runs `clang-tidy` on the code in the `SDL` directory to performs the checks specified in `.clang-tidy`.
-- `make check-fix`
-  Same as `make check`, but fixes the issues that it knows how to fix.
- 
\ No newline at end of file
+```
+scram b -j 12 code-checks >& c.log && scram b -j 12 code-format >& f.log
+```
\ No newline at end of file
diff --git a/RecoTracker/LSTCore/standalone/analysis/DNN/train_T5_DNN.ipynb b/RecoTracker/LSTCore/standalone/analysis/DNN/train_T5_DNN.ipynb
new file mode 100644
index 0000000000000..e7ec1b45283e5
--- /dev/null
+++ b/RecoTracker/LSTCore/standalone/analysis/DNN/train_T5_DNN.ipynb
@@ -0,0 +1,1073 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import uproot\n",
+    "import numpy as np\n",
+    "\n",
+    "def load_root_file(file_path, branches=None, print_branches=False):\n",
+    "    all_branches = {}\n",
+    "    with uproot.open(file_path) as file:\n",
+    "        tree = file[\"tree\"]\n",
+    "        # Load all ROOT branches into array if not specified\n",
+    "        if branches is None:\n",
+    "            branches = tree.keys()\n",
+    "        # Option to print the branch names\n",
+    "        if print_branches:\n",
+    "            print(\"Branches:\", tree.keys())\n",
+    "        # Each branch is added to the dictionary\n",
+    "        for branch in branches:\n",
+    "            try:\n",
+    "                all_branches[branch] = (tree[branch].array(library=\"np\"))\n",
+    "            except uproot.KeyInFileError as e:\n",
+    "                print(f\"KeyInFileError: {e}\")\n",
+    "        # Number of events in file\n",
+    "        all_branches['event'] = tree.num_entries\n",
+    "    return all_branches\n",
+    "\n",
+    "branches_list = [\n",
+    "    't5_innerRadius',\n",
+    "    't5_bridgeRadius',\n",
+    "    't5_outerRadius',\n",
+    "    't5_pt',\n",
+    "    't5_eta',\n",
+    "    't5_phi',\n",
+    "    't5_isFake',\n",
+    "    't5_t3_idx0',\n",
+    "    't5_t3_idx1',\n",
+    "    't5_pMatched',\n",
+    "    't5_sim_vxy',\n",
+    "    't5_sim_vz'\n",
+    "]\n",
+    "\n",
+    "# Hit-dependent branches\n",
+    "suffixes = ['r', 'z', 'eta', 'phi', 'layer']\n",
+    "branches_list += [f't5_t3_{i}_{suffix}' for i in [0, 2, 4] for suffix in suffixes]\n",
+    "\n",
+    "file_path = \"1000_no_dnn_for_phi.root\"\n",
+    "branches = load_root_file(file_path, branches_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Z max: 267.2349853515625, R max: 110.10993957519531, Eta max: 2.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "z_max = np.max([np.max(event) for event in branches[f't5_t3_4_z']])\n",
+    "r_max = np.max([np.max(event) for event in branches[f't5_t3_4_r']])\n",
+    "eta_max = 2.5\n",
+    "phi_max = np.pi\n",
+    "\n",
+    "print(f'Z max: {z_max}, R max: {r_max}, Eta max: {eta_max}')\n",
+    "\n",
+    "def delta_phi(phi1, phi2):\n",
+    "    delta = phi1 - phi2\n",
+    "    # Adjust delta to be within the range [-pi, pi]\n",
+    "    if delta > np.pi:\n",
+    "        delta -= 2 * np.pi\n",
+    "    elif delta < -np.pi:\n",
+    "        delta += 2 * np.pi\n",
+    "    return delta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_list = []\n",
+    "eta_list = [] # Used for DNN cut values\n",
+    "\n",
+    "for event in range(branches['event']):\n",
+    "    # Determine the number of elements in this event\n",
+    "    num_elements = len(branches['t5_t3_idx0'][event])\n",
+    "\n",
+    "    for i in range(num_elements):\n",
+    "        features_iter = []\n",
+    "        eta_iter = []\n",
+    "        \n",
+    "        idx0 = branches['t5_t3_idx0'][event][i]\n",
+    "        idx1 = branches['t5_t3_idx1'][event][i]\n",
+    "\n",
+    "        eta1 = np.abs(branches['t5_t3_0_eta'][event][idx0])\n",
+    "        eta2 = np.abs(branches['t5_t3_2_eta'][event][idx0])\n",
+    "        eta3 = np.abs(branches['t5_t3_4_eta'][event][idx0])\n",
+    "        eta4 = np.abs(branches['t5_t3_2_eta'][event][idx1])\n",
+    "        eta5 = np.abs(branches['t5_t3_4_eta'][event][idx1])\n",
+    "\n",
+    "        phi1 = (branches['t5_t3_0_phi'][event][idx0])\n",
+    "        phi2 = (branches['t5_t3_2_phi'][event][idx0])\n",
+    "        phi3 = (branches['t5_t3_4_phi'][event][idx0])\n",
+    "        phi4 = (branches['t5_t3_2_phi'][event][idx1])\n",
+    "        phi5 = (branches['t5_t3_4_phi'][event][idx1])\n",
+    "\n",
+    "        z1 = np.abs(branches['t5_t3_0_z'][event][idx0])\n",
+    "        z2 = np.abs(branches['t5_t3_2_z'][event][idx0])\n",
+    "        z3 = np.abs(branches['t5_t3_4_z'][event][idx0])\n",
+    "        z4 = np.abs(branches['t5_t3_2_z'][event][idx1])\n",
+    "        z5 = np.abs(branches['t5_t3_4_z'][event][idx1])\n",
+    "\n",
+    "        r1 = branches['t5_t3_0_r'][event][idx0]\n",
+    "        r2 = branches['t5_t3_2_r'][event][idx0]\n",
+    "        r3 = branches['t5_t3_4_r'][event][idx0]\n",
+    "        r4 = branches['t5_t3_2_r'][event][idx1]\n",
+    "        r5 = branches['t5_t3_4_r'][event][idx1]\n",
+    "\n",
+    "        innerRad = branches['t5_innerRadius'][event][i]\n",
+    "        bridgeRad = branches['t5_bridgeRadius'][event][i]\n",
+    "        outerRad = branches['t5_outerRadius'][event][i]\n",
+    "\n",
+    "        # Construct the input feature vector using pairwise differences\n",
+    "        features_iter = [\n",
+    "            eta1 / eta_max,                      # First hit eta, normalized\n",
+    "            np.abs(phi1) / phi_max,              # First hit phi, normalized\n",
+    "            z1 / z_max,                          # First hit z, normalized\n",
+    "            r1 / r_max,                          # First hit r, normalized\n",
+    "\n",
+    "            eta2 - eta1,                         # Difference in eta between hit 2 and 1\n",
+    "            delta_phi(phi2, phi1) / phi_max,     # Difference in phi between hit 2 and 1\n",
+    "            (z2 - z1) / z_max,                   # Difference in z between hit 2 and 1, normalized\n",
+    "            (r2 - r1) / r_max,                   # Difference in r between hit 2 and 1, normalized\n",
+    "\n",
+    "            eta3 - eta2,                         # Difference in eta between hit 3 and 2\n",
+    "            delta_phi(phi3, phi2) / phi_max,     # Difference in phi between hit 3 and 2\n",
+    "            (z3 - z2) / z_max,                   # Difference in z between hit 3 and 2, normalized\n",
+    "            (r3 - r2) / r_max,                   # Difference in r between hit 3 and 2, normalized\n",
+    "\n",
+    "            eta4 - eta3,                         # Difference in eta between hit 4 and 3\n",
+    "            delta_phi(phi4, phi3) / phi_max,     # Difference in phi between hit 4 and 3\n",
+    "            (z4 - z3) / z_max,                   # Difference in z between hit 4 and 3, normalized\n",
+    "            (r4 - r3) / r_max,                   # Difference in r between hit 4 and 3, normalized\n",
+    "\n",
+    "            eta5 - eta4,                         # Difference in eta between hit 5 and 4\n",
+    "            delta_phi(phi5, phi4) / phi_max,     # Difference in phi between hit 5 and 4\n",
+    "            (z5 - z4) / z_max,                   # Difference in z between hit 5 and 4, normalized\n",
+    "            (r5 - r4) / r_max,                   # Difference in r between hit 5 and 4, normalized\n",
+    "\n",
+    "            np.log10(innerRad),\n",
+    "            np.log10(bridgeRad),\n",
+    "            np.log10(outerRad)\n",
+    "        ]\n",
+    "\n",
+    "        # Use the abs eta value of first hit to select cut thresholds\n",
+    "        eta_iter.extend([np.abs(branches['t5_t3_0_eta'][event][idx0])])\n",
+    "        \n",
+    "        # Append the feature vector to the list\n",
+    "        features_list.append(features_iter)\n",
+    "        eta_list.append(eta_iter)\n",
+    "\n",
+    "# Convert the list of features to a NumPy array\n",
+    "features = np.array(features_list).T\n",
+    "eta_list = np.array(eta_list).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "# Stack features along a new axis to form a single array suitable for NN input\n",
+    "input_features_numpy = np.stack(features, axis=-1)\n",
+    "\n",
+    "# Identify rows with NaN or Inf values\n",
+    "mask = ~np.isnan(input_features_numpy) & ~np.isinf(input_features_numpy)\n",
+    "\n",
+    "# Apply mask across all columns: retain a row only if all its entries are neither NaN nor Inf\n",
+    "filtered_input_features_numpy = input_features_numpy[np.all(mask, axis=1)]\n",
+    "t5_isFake_filtered = np.concatenate(branches['t5_isFake'])[np.all(mask, axis=1)]\n",
+    "\n",
+    "# Convert to PyTorch tensor when ready to use with NN\n",
+    "input_features_tensor = torch.tensor(filtered_input_features_numpy, dtype=torch.float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using device: cuda\n",
+      "Initial dataset size: 10921577\n",
+      "Dataset size after initial 100.0% downsampling: 10921577\n",
+      "Class distribution after initial downsampling - Class 0: 2985946, Class 1: 7935631\n",
+      "Final class distribution after balancing - Class 0: 2985946, Class 1: 2985946\n",
+      "Epoch [1/150], Loss: 0.4802, Test Acc: 81.32%\n",
+      "Epoch [2/150], Loss: 0.4255, Test Acc: 81.45%\n",
+      "Epoch [3/150], Loss: 0.4602, Test Acc: 82.69%\n",
+      "Epoch [4/150], Loss: 0.4475, Test Acc: 83.97%\n",
+      "Epoch [5/150], Loss: 0.4417, Test Acc: 82.76%\n",
+      "Epoch [6/150], Loss: 0.4643, Test Acc: 83.01%\n",
+      "Epoch [7/150], Loss: 0.4474, Test Acc: 81.68%\n",
+      "Epoch [8/150], Loss: 0.4050, Test Acc: 84.32%\n",
+      "Epoch [9/150], Loss: 0.3563, Test Acc: 83.51%\n",
+      "Epoch [10/150], Loss: 0.4774, Test Acc: 83.24%\n",
+      "Epoch [11/150], Loss: 0.4846, Test Acc: 84.06%\n",
+      "Epoch [12/150], Loss: 0.5055, Test Acc: 83.28%\n",
+      "Epoch [13/150], Loss: 0.4461, Test Acc: 84.35%\n",
+      "Epoch [14/150], Loss: 0.4607, Test Acc: 82.75%\n",
+      "Epoch [15/150], Loss: 0.4795, Test Acc: 83.22%\n",
+      "Epoch [16/150], Loss: 0.4414, Test Acc: 83.83%\n",
+      "Epoch [17/150], Loss: 0.4357, Test Acc: 82.26%\n",
+      "Epoch [18/150], Loss: 0.4498, Test Acc: 83.85%\n",
+      "Epoch [19/150], Loss: 0.4779, Test Acc: 84.34%\n",
+      "Epoch [20/150], Loss: 0.4248, Test Acc: 83.84%\n",
+      "Epoch [21/150], Loss: 0.4538, Test Acc: 83.97%\n",
+      "Epoch [22/150], Loss: 0.4335, Test Acc: 84.28%\n",
+      "Epoch [23/150], Loss: 0.4248, Test Acc: 84.43%\n",
+      "Epoch [24/150], Loss: 0.4072, Test Acc: 83.57%\n",
+      "Epoch [25/150], Loss: 0.3732, Test Acc: 83.51%\n",
+      "Epoch [26/150], Loss: 0.4559, Test Acc: 83.91%\n",
+      "Epoch [27/150], Loss: 0.4071, Test Acc: 83.04%\n",
+      "Epoch [28/150], Loss: 0.4709, Test Acc: 84.22%\n",
+      "Epoch [29/150], Loss: 0.4079, Test Acc: 83.83%\n",
+      "Epoch [30/150], Loss: 0.3676, Test Acc: 83.50%\n",
+      "Epoch [31/150], Loss: 0.4944, Test Acc: 83.84%\n",
+      "Epoch [32/150], Loss: 0.4316, Test Acc: 84.63%\n",
+      "Epoch [33/150], Loss: 0.4229, Test Acc: 83.43%\n",
+      "Epoch [34/150], Loss: 0.3955, Test Acc: 83.82%\n",
+      "Epoch [35/150], Loss: 0.4467, Test Acc: 83.64%\n",
+      "Epoch [36/150], Loss: 0.4164, Test Acc: 84.49%\n",
+      "Epoch [37/150], Loss: 0.4656, Test Acc: 83.64%\n",
+      "Epoch [38/150], Loss: 0.4274, Test Acc: 84.10%\n",
+      "Epoch [39/150], Loss: 0.3943, Test Acc: 84.63%\n",
+      "Epoch [40/150], Loss: 0.4476, Test Acc: 84.55%\n",
+      "Epoch [41/150], Loss: 0.4765, Test Acc: 84.51%\n",
+      "Epoch [42/150], Loss: 0.4007, Test Acc: 84.32%\n",
+      "Epoch [43/150], Loss: 0.4716, Test Acc: 84.01%\n",
+      "Epoch [44/150], Loss: 0.4063, Test Acc: 83.81%\n",
+      "Epoch [45/150], Loss: 0.3712, Test Acc: 84.63%\n",
+      "Epoch [46/150], Loss: 0.3657, Test Acc: 84.43%\n",
+      "Epoch [47/150], Loss: 0.4514, Test Acc: 84.26%\n",
+      "Epoch [48/150], Loss: 0.3817, Test Acc: 84.30%\n",
+      "Epoch [49/150], Loss: 0.3744, Test Acc: 83.23%\n",
+      "Epoch [50/150], Loss: 0.4377, Test Acc: 84.43%\n",
+      "Epoch [51/150], Loss: 0.4331, Test Acc: 84.19%\n",
+      "Epoch [52/150], Loss: 0.4022, Test Acc: 84.39%\n",
+      "Epoch [53/150], Loss: 0.4272, Test Acc: 84.67%\n",
+      "Epoch [54/150], Loss: 0.4146, Test Acc: 84.06%\n",
+      "Epoch [55/150], Loss: 0.3798, Test Acc: 84.52%\n",
+      "Epoch [56/150], Loss: 0.4070, Test Acc: 83.82%\n",
+      "Epoch [57/150], Loss: 0.5018, Test Acc: 84.64%\n",
+      "Epoch [58/150], Loss: 0.5112, Test Acc: 84.71%\n",
+      "Epoch [59/150], Loss: 0.4554, Test Acc: 84.41%\n",
+      "Epoch [60/150], Loss: 0.4313, Test Acc: 84.78%\n",
+      "Epoch [61/150], Loss: 0.4101, Test Acc: 83.46%\n",
+      "Epoch [62/150], Loss: 0.4139, Test Acc: 84.60%\n",
+      "Epoch [63/150], Loss: 0.3841, Test Acc: 84.47%\n",
+      "Epoch [64/150], Loss: 0.4931, Test Acc: 83.95%\n",
+      "Epoch [65/150], Loss: 0.3589, Test Acc: 84.59%\n",
+      "Epoch [66/150], Loss: 0.4328, Test Acc: 84.87%\n",
+      "Epoch [67/150], Loss: 0.4525, Test Acc: 84.00%\n",
+      "Epoch [68/150], Loss: 0.4745, Test Acc: 84.31%\n",
+      "Epoch [69/150], Loss: 0.4585, Test Acc: 84.07%\n",
+      "Epoch [70/150], Loss: 0.4348, Test Acc: 84.95%\n",
+      "Epoch [71/150], Loss: 0.4101, Test Acc: 85.12%\n",
+      "Epoch [72/150], Loss: 0.4001, Test Acc: 84.77%\n",
+      "Epoch [73/150], Loss: 0.4505, Test Acc: 84.56%\n",
+      "Epoch [74/150], Loss: 0.3493, Test Acc: 84.78%\n",
+      "Epoch [75/150], Loss: 0.4316, Test Acc: 83.27%\n",
+      "Epoch [76/150], Loss: 0.4963, Test Acc: 84.52%\n",
+      "Epoch [77/150], Loss: 0.4214, Test Acc: 84.66%\n",
+      "Epoch [78/150], Loss: 0.5551, Test Acc: 84.71%\n",
+      "Epoch [79/150], Loss: 0.3809, Test Acc: 84.12%\n",
+      "Epoch [80/150], Loss: 0.3979, Test Acc: 84.31%\n",
+      "Epoch [81/150], Loss: 0.3920, Test Acc: 84.49%\n",
+      "Epoch [82/150], Loss: 0.4278, Test Acc: 84.71%\n",
+      "Epoch [83/150], Loss: 0.3696, Test Acc: 84.69%\n",
+      "Epoch [84/150], Loss: 0.3483, Test Acc: 84.02%\n",
+      "Epoch [85/150], Loss: 0.3976, Test Acc: 84.13%\n",
+      "Epoch [86/150], Loss: 0.3335, Test Acc: 84.92%\n",
+      "Epoch [87/150], Loss: 0.3972, Test Acc: 84.58%\n",
+      "Epoch [88/150], Loss: 0.4135, Test Acc: 84.32%\n",
+      "Epoch [89/150], Loss: 0.4556, Test Acc: 84.21%\n",
+      "Epoch [90/150], Loss: 0.4180, Test Acc: 84.28%\n",
+      "Epoch [91/150], Loss: 0.3586, Test Acc: 84.78%\n",
+      "Epoch [92/150], Loss: 0.4388, Test Acc: 84.43%\n",
+      "Epoch [93/150], Loss: 0.4243, Test Acc: 84.12%\n",
+      "Epoch [94/150], Loss: 0.4133, Test Acc: 84.55%\n",
+      "Epoch [95/150], Loss: 0.4201, Test Acc: 84.86%\n",
+      "Epoch [96/150], Loss: 0.4670, Test Acc: 84.50%\n",
+      "Epoch [97/150], Loss: 0.4199, Test Acc: 84.89%\n",
+      "Epoch [98/150], Loss: 0.4076, Test Acc: 84.35%\n",
+      "Epoch [99/150], Loss: 0.3696, Test Acc: 84.98%\n",
+      "Epoch [100/150], Loss: 0.3553, Test Acc: 84.70%\n",
+      "Epoch [101/150], Loss: 0.4054, Test Acc: 84.40%\n",
+      "Epoch [102/150], Loss: 0.4168, Test Acc: 84.65%\n",
+      "Epoch [103/150], Loss: 0.3675, Test Acc: 84.69%\n",
+      "Epoch [104/150], Loss: 0.4107, Test Acc: 84.10%\n",
+      "Epoch [105/150], Loss: 0.4310, Test Acc: 84.55%\n",
+      "Epoch [106/150], Loss: 0.4340, Test Acc: 83.76%\n",
+      "Epoch [107/150], Loss: 0.4691, Test Acc: 84.93%\n",
+      "Epoch [108/150], Loss: 0.4838, Test Acc: 84.37%\n",
+      "Epoch [109/150], Loss: 0.4057, Test Acc: 84.73%\n",
+      "Epoch [110/150], Loss: 0.4900, Test Acc: 84.75%\n",
+      "Epoch [111/150], Loss: 0.4501, Test Acc: 84.18%\n",
+      "Epoch [112/150], Loss: 0.4528, Test Acc: 84.51%\n",
+      "Epoch [113/150], Loss: 0.4126, Test Acc: 84.74%\n",
+      "Epoch [114/150], Loss: 0.4142, Test Acc: 84.99%\n",
+      "Epoch [115/150], Loss: 0.3894, Test Acc: 84.30%\n",
+      "Epoch [116/150], Loss: 0.4518, Test Acc: 84.30%\n",
+      "Epoch [117/150], Loss: 0.4568, Test Acc: 84.43%\n",
+      "Epoch [118/150], Loss: 0.3588, Test Acc: 84.32%\n",
+      "Epoch [119/150], Loss: 0.3891, Test Acc: 84.38%\n",
+      "Epoch [120/150], Loss: 0.4558, Test Acc: 84.62%\n",
+      "Epoch [121/150], Loss: 0.4732, Test Acc: 84.41%\n",
+      "Epoch [122/150], Loss: 0.4008, Test Acc: 84.54%\n",
+      "Epoch [123/150], Loss: 0.4279, Test Acc: 84.21%\n",
+      "Epoch [124/150], Loss: 0.4658, Test Acc: 84.58%\n",
+      "Epoch [125/150], Loss: 0.4696, Test Acc: 84.59%\n",
+      "Epoch [126/150], Loss: 0.4663, Test Acc: 84.00%\n",
+      "Epoch [127/150], Loss: 0.3993, Test Acc: 84.53%\n",
+      "Epoch [128/150], Loss: 0.4316, Test Acc: 84.31%\n",
+      "Epoch [129/150], Loss: 0.4189, Test Acc: 84.63%\n",
+      "Epoch [130/150], Loss: 0.3826, Test Acc: 83.96%\n",
+      "Epoch [131/150], Loss: 0.3437, Test Acc: 84.45%\n",
+      "Epoch [132/150], Loss: 0.4950, Test Acc: 85.07%\n",
+      "Epoch [133/150], Loss: 0.4394, Test Acc: 84.15%\n",
+      "Epoch [134/150], Loss: 0.3998, Test Acc: 84.38%\n",
+      "Epoch [135/150], Loss: 0.3154, Test Acc: 84.99%\n",
+      "Epoch [136/150], Loss: 0.4408, Test Acc: 84.83%\n",
+      "Epoch [137/150], Loss: 0.4970, Test Acc: 84.38%\n",
+      "Epoch [138/150], Loss: 0.4473, Test Acc: 84.13%\n",
+      "Epoch [139/150], Loss: 0.4615, Test Acc: 84.66%\n",
+      "Epoch [140/150], Loss: 0.4316, Test Acc: 84.38%\n",
+      "Epoch [141/150], Loss: 0.5141, Test Acc: 84.62%\n",
+      "Epoch [142/150], Loss: 0.4030, Test Acc: 84.03%\n",
+      "Epoch [143/150], Loss: 0.4777, Test Acc: 84.15%\n",
+      "Epoch [144/150], Loss: 0.4286, Test Acc: 84.78%\n",
+      "Epoch [145/150], Loss: 0.4194, Test Acc: 84.73%\n",
+      "Epoch [146/150], Loss: 0.3649, Test Acc: 84.84%\n",
+      "Epoch [147/150], Loss: 0.4346, Test Acc: 84.00%\n",
+      "Epoch [148/150], Loss: 0.4373, Test Acc: 84.60%\n",
+      "Epoch [149/150], Loss: 0.4238, Test Acc: 84.78%\n",
+      "Epoch [150/150], Loss: 0.4499, Test Acc: 84.80%\n"
+     ]
+    }
+   ],
+   "source": [
+    "from torch import nn\n",
+    "from torch.optim import Adam\n",
+    "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
+    "import torch\n",
+    "\n",
+    "# Set device\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Using device: {device}\")\n",
+    "\n",
+    "# Create labels tensor\n",
+    "labels_tensor = 1 - torch.tensor(t5_isFake_filtered, dtype=torch.float32)\n",
+    "\n",
+    "# Set initial downsample fraction\n",
+    "initial_downsample_fraction = 1.0  # Adjust this value as needed\n",
+    "\n",
+    "class MyNeuralNetwork(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(MyNeuralNetwork, self).__init__()\n",
+    "        self.layer1 = nn.Linear(input_features_numpy.shape[1], 32)\n",
+    "        self.layer2 = nn.Linear(32, 32)\n",
+    "        self.output_layer = nn.Linear(32, 1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.layer1(x)\n",
+    "        x = nn.ReLU()(x)\n",
+    "        x = self.layer2(x)\n",
+    "        x = nn.ReLU()(x)\n",
+    "        x = self.output_layer(x)\n",
+    "        x = torch.sigmoid(x)\n",
+    "        return x\n",
+    "\n",
+    "class WeightedBCELoss(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(WeightedBCELoss, self).__init__()\n",
+    "        \n",
+    "    def forward(self, outputs, targets, weights):\n",
+    "        eps = 1e-7\n",
+    "        losses = -(weights * (targets * torch.log(outputs + eps) + \n",
+    "                            (1 - targets) * torch.log(1 - outputs + eps)))\n",
+    "        return losses.mean()\n",
+    "\n",
+    "def calculate_sample_weights(t5_sim_vxy, weight_factor=6.0):\n",
+    "    \"\"\"\n",
+    "    Calculate sample weights giving higher importance to displaced t5's\n",
+    "    \n",
+    "    Args:\n",
+    "        t5_sim_vxy: Array of t5 simulation values\n",
+    "        weight_factor: How much more weight to give to displaced samples\n",
+    "    \n",
+    "    Returns:\n",
+    "        Tensor of sample weights\n",
+    "    \"\"\"\n",
+    "    weights = torch.ones(len(t5_sim_vxy))\n",
+    "    displaced_mask = t5_sim_vxy > 0.1\n",
+    "    weights[displaced_mask] = weight_factor\n",
+    "    return weights\n",
+    "\n",
+    "# Print initial dataset size\n",
+    "print(f\"Initial dataset size: {len(labels_tensor)}\")\n",
+    "\n",
+    "# Calculate sample weights\n",
+    "sample_weights = calculate_sample_weights(torch.tensor(np.concatenate(branches['t5_sim_vxy'])))\n",
+    "\n",
+    "# Remove rows with NaN and update weights accordingly\n",
+    "nan_mask = torch.isnan(input_features_tensor).any(dim=1) | torch.isnan(labels_tensor)\n",
+    "filtered_inputs = input_features_tensor[~nan_mask]\n",
+    "filtered_labels = labels_tensor[~nan_mask]\n",
+    "filtered_weights = sample_weights[~nan_mask]\n",
+    "\n",
+    "# Initial downsampling of entire dataset\n",
+    "if initial_downsample_fraction < 1.0:\n",
+    "    total_samples = len(filtered_labels)\n",
+    "    samples_to_keep = int(total_samples * initial_downsample_fraction)\n",
+    "    indices = torch.randperm(total_samples)[:samples_to_keep]\n",
+    "    filtered_inputs = filtered_inputs[indices]\n",
+    "    filtered_labels = filtered_labels[indices]\n",
+    "    filtered_weights = filtered_weights[indices]\n",
+    "\n",
+    "print(f\"Dataset size after initial {initial_downsample_fraction*100}% downsampling: {len(filtered_labels)}\")\n",
+    "\n",
+    "# Count samples in each class after initial downsampling\n",
+    "class_counts = torch.bincount(filtered_labels.int())\n",
+    "print(f\"Class distribution after initial downsampling - Class 0: {class_counts[0]}, Class 1: {class_counts[1]}\")\n",
+    "\n",
+    "# Balance classes while maintaining weights\n",
+    "minority_class = 0 if class_counts[0] < class_counts[1] else 1\n",
+    "minority_indices = (filtered_labels == minority_class).nonzero(as_tuple=True)[0]\n",
+    "majority_indices = (filtered_labels == (1 - minority_class)).nonzero(as_tuple=True)[0]\n",
+    "downsampled_majority_indices = majority_indices[torch.randperm(len(majority_indices))[:len(minority_indices)]]\n",
+    "balanced_indices = torch.cat((minority_indices, downsampled_majority_indices))\n",
+    "\n",
+    "# Create balanced dataset with weights\n",
+    "balanced_inputs = filtered_inputs[balanced_indices]\n",
+    "balanced_labels = filtered_labels[balanced_indices]\n",
+    "balanced_weights = filtered_weights[balanced_indices]\n",
+    "\n",
+    "# Verify balanced distribution\n",
+    "balanced_counts = torch.bincount(balanced_labels.int())\n",
+    "print(f\"Final class distribution after balancing - Class 0: {balanced_counts[0]}, Class 1: {balanced_counts[1]}\")\n",
+    "\n",
+    "# Create dataset with weights\n",
+    "dataset = TensorDataset(balanced_inputs, balanced_labels, balanced_weights)\n",
+    "\n",
+    "# Split into train and test sets\n",
+    "train_size = int(0.8 * len(dataset))\n",
+    "test_size = len(dataset) - train_size\n",
+    "train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
+    "\n",
+    "# Create data loaders\n",
+    "train_loader = DataLoader(train_dataset, batch_size=1024, shuffle=True, num_workers=10, pin_memory=True)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=1024, shuffle=False, num_workers=10, pin_memory=True)\n",
+    "\n",
+    "# Initialize model and optimizer\n",
+    "model = MyNeuralNetwork().to(device)\n",
+    "loss_function = WeightedBCELoss()\n",
+    "optimizer = Adam(model.parameters(), lr=0.0025)\n",
+    "\n",
+    "def evaluate_model(loader):\n",
+    "    model.eval()\n",
+    "    total = 0\n",
+    "    correct = 0\n",
+    "    with torch.no_grad():\n",
+    "        for inputs, targets, weights in loader:\n",
+    "            inputs, targets = inputs.to(device), targets.to(device)\n",
+    "            outputs = model(inputs)\n",
+    "            predicted = outputs.squeeze() > 0.5\n",
+    "            total += targets.size(0)\n",
+    "            correct += (predicted == targets.bool()).sum().item()\n",
+    "    model.train()\n",
+    "    return 100 * correct / total\n",
+    "\n",
+    "# Training loop\n",
+    "num_epochs = 150\n",
+    "loss_log = []\n",
+    "\n",
+    "for epoch in range(num_epochs):\n",
+    "    for inputs, targets, weights in train_loader:\n",
+    "        inputs, targets, weights = inputs.to(device), targets.to(device), weights.to(device)\n",
+    "    \n",
+    "        # Forward pass\n",
+    "        outputs = model(inputs)\n",
+    "        loss = loss_function(outputs.squeeze(), targets, weights)\n",
+    "        \n",
+    "        loss_log.append(loss.item())\n",
+    "\n",
+    "        # Backward and optimize\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "    test_accuracy = evaluate_model(test_loader)\n",
+    "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}, Test Acc: {test_accuracy:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(model.state_dict(), \"model.pth\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline accuracy: 0.8745944499969482\n",
+      "Feature importances:\n",
+      "Feature 21 importance: 0.3800\n",
+      "Feature 20 importance: 0.2052\n",
+      "Feature 0 importance: 0.2036\n",
+      "Feature 22 importance: 0.1572\n",
+      "Feature 17 importance: 0.1333\n",
+      "Feature 12 importance: 0.1323\n",
+      "Feature 13 importance: 0.1207\n",
+      "Feature 5 importance: 0.1142\n",
+      "Feature 2 importance: 0.0741\n",
+      "Feature 16 importance: 0.0638\n",
+      "Feature 15 importance: 0.0420\n",
+      "Feature 8 importance: 0.0402\n",
+      "Feature 9 importance: 0.0399\n",
+      "Feature 6 importance: 0.0305\n",
+      "Feature 7 importance: 0.0274\n",
+      "Feature 4 importance: 0.0269\n",
+      "Feature 3 importance: 0.0247\n",
+      "Feature 14 importance: 0.0162\n",
+      "Feature 10 importance: 0.0128\n",
+      "Feature 19 importance: 0.0117\n",
+      "Feature 11 importance: 0.0106\n",
+      "Feature 18 importance: 0.0089\n",
+      "Feature 1 importance: 0.0001\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# Convert tensors to numpy for simplicity in permutation\n",
+    "input_features_np = input_features_tensor.numpy()\n",
+    "labels_np = labels_tensor.numpy()\n",
+    "\n",
+    "def model_accuracy(features, labels, model):\n",
+    "    model.eval()  # Set the model to evaluation mode\n",
+    "    inputs = features.to(device)\n",
+    "    labels = labels.to(device)\n",
+    "    with torch.no_grad():\n",
+    "        outputs = model(inputs)\n",
+    "        predicted = (outputs.squeeze() > 0.5).float()  # Update threshold as necessary\n",
+    "        accuracy = (predicted == labels).float().mean().item()\n",
+    "    return accuracy\n",
+    "\n",
+    "# Use the original input_features_tensor and labels_tensor directly\n",
+    "baseline_accuracy = model_accuracy(input_features_tensor, labels_tensor, model)\n",
+    "print(f\"Baseline accuracy: {baseline_accuracy}\")\n",
+    "\n",
+    "# Initialize an array to store feature importances\n",
+    "feature_importances = np.zeros(input_features_tensor.shape[1])\n",
+    "\n",
+    "# Permute each feature and calculate the drop in accuracy\n",
+    "for i in range(input_features_tensor.shape[1]):\n",
+    "    permuted_features = input_features_tensor.clone()\n",
+    "    permuted_features[:, i] = permuted_features[torch.randperm(permuted_features.size(0)), i]  # Permute feature\n",
+    "\n",
+    "    permuted_accuracy = model_accuracy(permuted_features, labels_tensor, model)\n",
+    "    feature_importances[i] = baseline_accuracy - permuted_accuracy\n",
+    "\n",
+    "# Ranking features by importance\n",
+    "important_features_indices = np.argsort(feature_importances)[::-1]  # Indices of features in descending importance\n",
+    "important_features_scores = np.sort(feature_importances)[::-1]  # Importance scores in descending order\n",
+    "\n",
+    "print(\"Feature importances:\")\n",
+    "for idx, score in zip(important_features_indices, important_features_scores):\n",
+    "    print(f\"Feature {idx} importance: {score:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_909590/52354147.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "  inputs = torch.tensor(features, dtype=torch.float32).to(device)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve, auc\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def model_outputs(features, model):\n",
+    "    model.eval()  # Set the model to evaluation mode\n",
+    "    with torch.no_grad():\n",
+    "        inputs = torch.tensor(features, dtype=torch.float32).to(device)\n",
+    "        outputs = model(inputs).squeeze().cpu().numpy()\n",
+    "    return outputs\n",
+    "\n",
+    "# Calculate model outputs\n",
+    "probabilities = model_outputs(filtered_inputs, model)\n",
+    "\n",
+    "# Calculate ROC curve and AUC\n",
+    "fpr, tpr, thresholds = roc_curve(filtered_labels, probabilities)\n",
+    "roc_auc = auc(fpr, tpr)\n",
+    "\n",
+    "# Plot ROC curve\n",
+    "plt.figure()\n",
+    "lw = 2  # Line width\n",
+    "plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.3f)' % roc_auc)\n",
+    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
+    "plt.xlim([0.0, 1.0])\n",
+    "plt.ylim([0.0, 1.05])\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('Receiver Operating Characteristic')\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_909590/52354147.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "  inputs = torch.tensor(features, dtype=torch.float32).to(device)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction scores for displaced tracks (t5_sim_vxy > 0.1):\n",
+      "Mean score: 0.8229\n",
+      "Median score: 0.9340\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get model predictions\n",
+    "probabilities = model_outputs(filtered_inputs, model)\n",
+    "\n",
+    "# Get displaced track mask\n",
+    "displaced_mask = np.concatenate(branches['t5_sim_vxy'])[~nan_mask] > 0.1\n",
+    "\n",
+    "# Calculate statistics for displaced tracks\n",
+    "displaced_predictions = probabilities[displaced_mask]\n",
+    "mean_score = np.mean(displaced_predictions)\n",
+    "median_score = np.median(displaced_predictions)\n",
+    "\n",
+    "print(f\"Prediction scores for displaced tracks (t5_sim_vxy > 0.1):\")\n",
+    "print(f\"Mean score: {mean_score:.4f}\")\n",
+    "print(f\"Median score: {median_score:.4f}\")\n",
+    "\n",
+    "plt.hist(displaced_predictions, bins=100, histtype='step', linewidth=1.5)  # Outline only, no fill\n",
+    "plt.yscale('log')\n",
+    "plt.xlabel(\"DNN Score\", fontsize=12)\n",
+    "plt.ylabel(\"Frequency (log scale)\", fontsize=12)\n",
+    "plt.title(\"DNN Score for Displaced T5s\", fontsize=14, weight='bold')\n",
+    "\n",
+    "plt.grid(visible=True, which='both', linestyle='--', linewidth=0.5)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_layer1[32] = {\n",
+      "-1.3837075f, -0.0653152f, -0.7900129f, 0.0714758f, -1.1574365f, -1.4634879f, -0.9317133f, -0.1455518f, -0.0459635f, -0.2055620f, 0.0586231f, -0.8943899f, -0.1009487f, 0.0166031f, -0.5451909f, -0.1384538f, 1.2664700f, -1.8996916f, -0.0025585f, -0.1647783f, -1.9019107f, 0.0707104f, -0.2373025f, 0.0357050f, -0.0048417f, 2.3127339f, -0.0508943f, -0.1116435f, -0.1610904f, -1.6463890f, -1.0739423f, -0.0962902f };\n",
+      "\n",
+      "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_layer1[23][32] = {\n",
+      "{ -0.1881404f, -0.0534256f, 1.6563641f, 0.0401664f, 2.8318353f, 1.5049738f, 1.4111555f, -0.2339872f, 0.0431970f, 0.1220361f, -0.0450153f, -1.6025578f, 0.0394025f, -0.3051167f, 1.9442217f, 0.1599094f, 0.1376955f, 2.4181051f, -0.0226484f, -0.1801709f, -0.4861264f, -0.0268545f, 0.5463807f, 0.2420150f, -0.1238829f, 0.2916382f, 0.1507791f, 0.7952659f, 0.2736979f, 3.2790639f, 1.2062043f, -0.0884467f },\n",
+      "{ -0.0469924f, 0.2013927f, 0.0307775f, -0.1241788f, -0.0100412f, 0.0422375f, 0.0211071f, -0.0359304f, 0.0451861f, 0.0291862f, -0.2094866f, -0.0013007f, 0.1191471f, 0.0750159f, 0.0184378f, 0.0419437f, -0.0207304f, -0.0444109f, 0.0013400f, -0.0699210f, -0.0668742f, -0.0880825f, -0.0107244f, 0.0363424f, 0.1391699f, -0.0112885f, -0.0060098f, -0.0073863f, -0.0566143f, -0.0224207f, 0.0103718f, -0.0015193f },\n",
+      "{ 0.4520382f, 0.1227609f, -1.3887709f, -0.0542129f, -3.2003114f, -0.8354173f, -1.3173198f, 0.3292131f, -0.1657729f, -0.1982902f, 0.1599589f, -0.0417666f, -0.1461042f, -1.3237997f, -5.3609071f, -0.0981676f, 0.2922535f, -1.8692241f, -0.0345302f, 0.1810613f, 0.4473544f, -0.0159401f, -0.7293931f, -1.4816793f, -0.1431545f, -0.0955672f, -0.2370718f, -0.7204540f, 0.8451244f, -3.4310548f, -1.3518151f, 0.1551731f },\n",
+      "{ 0.2670300f, 0.1343590f, 3.0347505f, -0.1783503f, 2.1586559f, 2.4137778f, 2.0080864f, -0.2545274f, -0.1985905f, 0.1653812f, -0.1714860f, 4.1022782f, -0.1045471f, 4.4776497f, 3.3737848f, -0.0849546f, -6.1899095f, 3.6970129f, 0.0007382f, 0.1675882f, 0.6014717f, -0.0287709f, 0.0495882f, 2.2192705f, -0.1043157f, -4.7508621f, -0.0022774f, 0.3766513f, -0.7505829f, 1.9759512f, 1.6747239f, -0.1004091f },\n",
+      "{ 0.6639504f, -0.0384022f, -10.0415087f, -0.0032648f, 0.3049855f, -2.0427964f, -1.1522077f, 0.0935732f, 0.1232134f, 0.0868663f, -0.0230848f, -1.8257296f, -0.0799238f, 6.8892417f, -1.3941933f, 0.0445172f, 0.9485117f, -2.5238073f, -0.0148513f, 0.2256772f, 0.5914315f, -0.1278037f, 0.1609928f, 11.3438406f, -0.0831544f, 0.1928522f, 0.0361467f, 0.0137040f, 4.9549832f, 2.3954937f, 0.3917757f, 0.1206975f },\n",
+      "{ 29.6590214f, -0.0836848f, -1.3028307f, -0.1391431f, -0.3703596f, 5.3762760f, 1.8429571f, 21.0697041f, -0.1232606f, 0.0066067f, -0.0308768f, -0.9960231f, 0.1865301f, -1.2142091f, 0.9273136f, 0.0974103f, 1.4067870f, 0.7268439f, 0.0035755f, 0.0619486f, -32.8901024f, -0.1950644f, -0.3978897f, -3.1790049f, -0.1371673f, 0.1569460f, 0.0268667f, -0.4512640f, 0.3055371f, -0.2241473f, -0.6455348f, 0.1178979f },\n",
+      "{ -2.9178317f, -0.2023720f, -0.2946439f, -0.1851392f, -0.3493766f, -1.5397958f, -1.5902523f, 1.0981250f, -0.1796725f, -0.0540953f, 0.0926500f, 2.0021629f, -0.1277778f, 3.3643394f, -7.5327554f, -0.0084912f, 2.7298651f, 0.2535582f, 0.0474618f, -0.1377846f, -2.2746830f, -0.2016302f, -0.7150622f, 4.4011140f, -0.1688751f, -1.2160714f, -0.0055839f, -1.1319760f, -2.2543004f, 0.6365916f, -1.4942099f, -0.0992425f },\n",
+      "{ -5.9751196f, -0.1597221f, -3.8946304f, 0.0537821f, 0.4741110f, 3.6895070f, 2.5116272f, 1.7058172f, -0.0860321f, -0.1519644f, 0.1465356f, 1.4165760f, -0.0984433f, 1.6990343f, 4.0953226f, 0.1742475f, -3.2570388f, 3.1653547f, 0.0135764f, 0.0092055f, -5.0966530f, -0.0542810f, 0.4907863f, 0.5900084f, -0.1736992f, -4.9153452f, 0.2017547f, 0.2854181f, 3.1490057f, 0.2885774f, 0.9775900f, -0.2207156f },\n",
+      "{ 0.3805595f, 0.0308984f, -9.5846119f, -0.0547350f, 1.9641919f, 2.0823991f, 9.9298115f, 0.0344243f, -0.1557834f, -0.1847700f, -0.1195207f, 4.4698248f, 0.1492174f, 0.4272707f, 4.7265644f, 0.0200772f, -14.3444443f, 4.9532328f, 0.0319610f, -0.0645846f, -0.6238102f, 0.1038110f, 0.2483765f, -5.1799927f, 0.0782294f, 16.8777409f, 0.0196593f, 0.8423936f, -8.5921221f, -0.0184179f, -5.7857180f, -0.0551181f },\n",
+      "{ 17.1570740f, 0.0265437f, -1.4766232f, -0.0528512f, 1.0128449f, 3.1529653f, -0.6560294f, 8.7189465f, -0.1728377f, 0.1245629f, 0.1072764f, 0.2649773f, 0.0254132f, -0.8094708f, 1.8371828f, 0.1586192f, 1.9410020f, 0.9662392f, -0.0839922f, -0.2894930f, -16.5091496f, -0.1079556f, -0.1204132f, -0.9694697f, 0.0537786f, 0.2476868f, 0.0076408f, 0.1025890f, 0.1267423f, 0.4956081f, 0.1457323f, 0.1342634f },\n",
+      "{ -0.5389574f, 0.1333421f, -4.6338782f, -0.0645123f, -0.6526322f, -3.2958410f, -1.2309581f, -1.0803053f, -0.1170542f, -0.0169311f, 0.1147491f, 2.9890807f, -0.1234096f, 0.6792320f, -3.9311285f, -0.0678321f, -2.7922039f, 4.9413238f, 0.1060735f, -0.1114068f, -2.2443752f, -0.1649915f, -0.3656403f, 2.5320942f, -0.0249616f, -4.5098810f, -0.1773834f, -1.9516623f, -1.6839710f, -0.1365123f, 1.0296160f, -0.0419825f },\n",
+      "{ -2.4413636f, 0.1075683f, -1.4518708f, 0.0537449f, 0.1154493f, -0.5463845f, 1.3964951f, 2.6729572f, -0.0206257f, 0.1435281f, -0.1819518f, 0.4540120f, -0.1910136f, 1.7696143f, 2.3670278f, 0.1324464f, -0.5837788f, -2.2784615f, 0.0345478f, -0.0980538f, -0.4999657f, 0.1178097f, 0.5756868f, -0.1058674f, 0.1920418f, -3.5473657f, 0.2146371f, 0.2557987f, 1.3935618f, 0.3242345f, 0.2029733f, -0.1844350f },\n",
+      "{ -0.9069599f, -0.2032758f, -0.5786582f, 0.1395915f, 3.9338124f, -1.6806563f, 0.4269728f, -0.3697720f, -0.0306356f, -0.0341866f, -0.0635755f, 1.8898975f, 0.1968578f, -17.2182655f, 1.4839698f, -0.0541308f, 15.9838457f, 18.5951862f, 0.0078872f, -0.1186571f, -2.4982276f, 0.0033835f, 0.3749593f, -15.0238085f, 0.0595601f, -16.8588371f, 0.1146287f, 0.1274172f, 19.3332062f, -7.0513921f, -5.4852023f, 0.1681230f },\n",
+      "{ -5.1457887f, 0.0335570f, 1.8620163f, 0.0560381f, -0.6397949f, -4.0867515f, 1.3578068f, -23.9992580f, -0.1034287f, 0.1437906f, 0.1076568f, -0.6930848f, -0.1176134f, 2.2855785f, -0.8021089f, 0.0424611f, -0.6139123f, -3.1381547f, 0.0188163f, -0.1728741f, 0.6676420f, -0.1124282f, 0.1077818f, 2.3839712f, 0.1340676f, 1.3538554f, 0.0421035f, 0.4513423f, -0.1543196f, 0.5120541f, -0.8940096f, -0.1175765f },\n",
+      "{ 2.1656792f, 0.1638565f, 4.5302448f, 0.0741160f, 3.3850696f, -4.8867540f, 2.8059542f, -0.0023008f, -0.1248942f, -0.0075225f, -0.0082212f, -1.0955724f, -0.1462416f, -1.7098176f, -4.1775723f, 0.1950609f, 3.6847639f, 1.6520064f, 0.0310502f, -0.0430167f, 3.4527576f, 0.1453262f, -1.0126116f, 1.8785841f, -0.0615105f, 1.0451943f, -0.2653875f, -1.2223006f, -1.0100641f, 1.2076828f, 0.4882897f, -0.0618375f },\n",
+      "{ 2.4578559f, -0.1464199f, -1.3086185f, 0.1208716f, -0.2079897f, -2.7138259f, -1.4107026f, -0.4483974f, -0.1599056f, 0.0242936f, 0.1326804f, 0.8664415f, 0.0588684f, 0.7366717f, 2.3159802f, -0.1917707f, -2.0800066f, -7.5100355f, 0.0585225f, 0.1582773f, 1.8128076f, -0.0756957f, 0.8521049f, 0.5539182f, -0.1738797f, -0.2020151f, 0.2219591f, 0.1088298f, -1.9535940f, 2.4130275f, -0.0741222f, 0.1156681f },\n",
+      "{ -0.4152933f, -0.0679605f, -0.5760314f, -0.0201883f, -14.1784763f, 0.7755737f, -19.5469246f, 0.0381304f, 0.0160074f, 0.1124380f, -0.0478151f, -2.3719466f, 0.0819727f, -12.5069208f, 2.0468810f, 0.0964909f, 7.8784809f, -6.3555703f, -0.0429914f, -0.0162720f, -0.9493829f, 0.0296786f, -0.0244959f, -12.6325788f, -0.1871653f, -9.8338795f, 0.0391840f, -0.1199073f, -11.7859421f, 8.7398720f, 19.4971046f, -0.1954873f },\n",
+      "{ -4.8962007f, -0.1695992f, 0.7760146f, -0.0199836f, -0.0576061f, -6.0196476f, -2.3023551f, -20.0125084f, -0.1957836f, -0.0993785f, 0.1109372f, -0.0710161f, -0.0553650f, 0.2546394f, -1.7578228f, 0.1498791f, -2.6269529f, 1.3973731f, 0.0464059f, -0.2307575f, 1.6730053f, -0.0038867f, 0.1040150f, 2.6721606f, 0.2027777f, -1.2358316f, -0.0587254f, 0.0610504f, -0.1700777f, -0.4323797f, 1.0359807f, -0.0127435f },\n",
+      "{ 1.1245984f, -0.1806923f, -1.5868790f, 0.1536594f, 1.6837788f, -1.6474472f, -3.9225550f, 0.4506312f, 0.1854908f, -0.1023232f, -0.0306957f, -0.8615071f, 0.0945480f, 2.0585704f, 0.6044773f, 0.1269336f, 2.4720187f, -4.5123949f, -0.0657749f, 0.1738364f, 2.4188614f, 0.0038840f, -0.2019601f, -0.3842189f, -0.0493631f, 3.6777370f, -0.1003436f, 0.6174496f, 1.0476112f, 2.7601521f, 0.9059890f, -0.1691816f },\n",
+      "{ 1.9658293f, 0.2083382f, 1.7833723f, 0.0662620f, -0.3932888f, -1.0642430f, 0.1807114f, -1.1486723f, -0.0177136f, -0.1706942f, 0.1730027f, 0.6712329f, 0.0485299f, 0.6379296f, -0.2880911f, -0.1993632f, -0.9471832f, 1.9425983f, 0.0328524f, 0.0777725f, 0.6454380f, 0.0143852f, 0.0192997f, 1.6793132f, -0.1872064f, -1.5757623f, 0.0242778f, -0.5992475f, 2.2148299f, -3.5215647f, -2.9748621f, 0.0112703f },\n",
+      "{ 0.3737165f, 0.0361593f, -0.1075856f, -0.0312021f, -0.0786010f, 1.3149793f, 0.0237401f, -0.0819654f, -0.1388431f, -0.0306386f, -0.0704427f, -2.3997226f, -0.1392045f, 0.7729424f, 0.1253861f, -0.0819755f, -0.7590774f, -0.3295609f, -0.0172208f, -0.0551179f, 0.4599459f, -0.1143881f, 2.7430685f, 0.3621114f, -0.1475701f, 0.2296079f, -2.2224922f, -0.9080986f, 0.2101683f, 0.1190262f, -2.2205217f, -0.0811555f },\n",
+      "{ 0.3946800f, -0.1204188f, 0.0543225f, -0.0392627f, 1.9454094f, 0.1865290f, 1.5276426f, -0.0342965f, 0.0117116f, -0.1873923f, -0.1045035f, 1.8535231f, -0.0207077f, 0.0981549f, -0.0327459f, -0.1486938f, 0.6359531f, -0.1314566f, -2.1469448f, -0.1665767f, 0.5134121f, -0.0341647f, -2.1786075f, -0.5976576f, 0.0111857f, 0.3272055f, 2.1917374f, -1.6247722f, 1.6025572f, -1.9965295f, 0.3347488f, 0.1113990f },\n",
+      "{ 0.0340557f, -0.1659652f, -0.0042457f, 0.0010229f, -2.1550148f, -0.4728722f, -1.3667214f, 0.2625635f, -0.0302200f, -0.0322885f, 0.0227866f, 0.6977839f, 0.0050141f, -1.6183628f, 0.0869662f, -0.0775411f, 0.4754244f, 0.4596581f, 2.1509945f, -0.0313832f, 0.0336208f, -0.1547154f, -0.6017126f, 0.0369996f, -0.1102583f, -0.5788267f, 0.0017006f, 2.6352038f, -1.7847317f, 1.7510574f, 2.1478791f, -0.2251654f },\n",
+      "};\n",
+      "\n",
+      "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_layer2[32] = {\n",
+      "-0.2689391f, 1.5461178f, -0.2424639f, 0.4424149f, -0.0411816f, -4.1070848f, 1.4709516f, -0.2439820f, -0.1750926f, 2.8802166f, -0.1573734f, -1.3724055f, 0.3671952f, 1.8267332f, 1.5655776f, -0.7323843f, 1.6318209f, 2.2198663f, -1.5951139f, -0.0870247f, 0.2806863f, -0.2407108f, 0.1310665f, -0.5246177f, 0.1914421f, -0.3386542f, -0.6310596f, 3.2995102f, 0.7519229f, -0.1565450f, -0.1496341f, 1.0073272f };\n",
+      "\n",
+      "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_layer2[32][32] = {\n",
+      "{ -0.1731049f, 1.7775618f, -0.2532010f, -0.2902778f, -0.1392802f, 4.2428946f, -0.1866968f, -0.1800365f, -0.0634398f, 0.0763313f, 0.0472901f, -0.8030146f, 0.3161853f, -1.0713238f, -4.6514492f, -0.3908085f, 1.1607268f, 0.8834935f, -0.1194544f, -0.0785166f, 0.4967587f, -0.0558136f, -0.9601135f, -0.1001592f, 3.4427991f, -0.2144053f, -0.3632556f, 0.0117088f, 0.1742481f, -0.2540179f, -0.1705156f, -0.2627344f },\n",
+      "{ -0.1478276f, -0.1659575f, 0.1602777f, -0.0758106f, 0.1067696f, -0.0247068f, -0.1123443f, -0.1724832f, -0.0013103f, -0.0685904f, 0.1537329f, 0.1042632f, -0.0360880f, -0.0679077f, 0.0672719f, 0.1597116f, -0.0150259f, 0.0367102f, -0.0545881f, -0.0693004f, -0.1008447f, -0.0672846f, -0.1395939f, -0.0324785f, -0.1051702f, -0.0530534f, -0.1019061f, -0.0921245f, 0.1195077f, 0.0453448f, 0.0257045f, -0.0622537f },\n",
+      "{ -0.0363173f, -0.1990481f, -0.0452148f, 0.4074381f, -0.0731660f, -0.0823270f, 0.3154473f, -0.1909118f, -0.0165690f, 0.1325824f, -0.0760181f, 0.7768906f, -0.2702211f, -0.6023573f, 1.5904741f, 0.2384946f, 0.7610655f, -2.8705251f, 0.5754877f, -0.1587478f, -0.5708794f, -0.3421216f, 0.5023443f, 1.2806857f, 0.2158970f, -0.1364033f, -0.3398291f, 0.9066412f, -1.2935438f, 0.0273695f, -0.1850613f, -0.9301611f },\n",
+      "{ -0.1281746f, 0.1695392f, 0.0805936f, -0.0598281f, 0.1266985f, -0.1697189f, -0.1091505f, -0.1569477f, 0.0363969f, -0.0628394f, 0.0107523f, 0.0659535f, -0.0568244f, -0.1299786f, 0.0005438f, -0.0806242f, -0.0806848f, -0.0919798f, -0.0748445f, 0.0792912f, 0.0022868f, 0.0211520f, -0.0183716f, 0.1279848f, -0.1518286f, -0.0113527f, 0.0824359f, -0.0178597f, 0.0272009f, 0.0288935f, 0.0123459f, 0.1685353f },\n",
+      "{ 0.1099675f, -0.3914332f, -0.0647218f, -0.8259028f, -0.0283726f, -0.0860217f, -2.0489185f, 0.1042144f, 0.1024824f, 0.0735443f, -0.1235109f, -3.3674469f, -0.1799957f, -7.1867313f, 1.6053666f, -0.5203959f, 0.8686391f, -0.0675404f, -2.8893898f, -0.0796400f, 1.2672142f, -0.0371844f, -1.8065344f, -2.2551982f, 0.0355568f, 0.0672171f, 0.7150316f, 1.3620002f, -0.4106106f, 0.0126076f, 0.0408083f, 1.5958146f },\n",
+      "{ 0.0525989f, 1.8947815f, -0.2513640f, -0.3715420f, -0.1752283f, 1.3911799f, -0.7633898f, -0.1716654f, -0.0145629f, -1.7601604f, -0.1943324f, -0.5716376f, -0.8281464f, -0.0308049f, -1.4709659f, -0.4294116f, -0.1030817f, -0.1823493f, 0.7561242f, -0.1608112f, 0.3980689f, -0.2464017f, -1.3065518f, 0.0875702f, -0.1504322f, -0.0352198f, -0.4051513f, 0.7010455f, -0.2363433f, -0.1118084f, -0.1329087f, -0.3257700f },\n",
+      "{ -0.1209070f, 0.1677164f, -0.1353413f, -0.0410048f, -0.1432644f, 0.2649301f, 0.2247741f, -0.0425357f, -0.2644008f, 1.4204332f, -0.2540753f, 0.2481354f, 1.9494507f, -0.2003033f, -0.5938342f, -0.3314930f, 1.5038266f, -2.4000788f, -1.6202501f, -0.0256936f, -0.2890913f, -0.2113032f, 0.9030544f, 1.1483711f, 0.0545346f, -0.1961582f, -0.2267976f, 0.2372836f, 2.5995049f, -0.1469661f, -0.1017130f, 1.6176132f },\n",
+      "{ 0.0542207f, 2.7658713f, -0.1700335f, -0.3357265f, -0.1097085f, 1.6508883f, 0.0132292f, 0.1211861f, -0.0852982f, 0.9232512f, 0.0202751f, 0.3138782f, 0.2674713f, 0.1247260f, 0.3859081f, 0.3961721f, 1.0556988f, 0.8574673f, -0.1462571f, -0.1600272f, 0.4117427f, -0.1561815f, 0.0553897f, -0.2753994f, 5.8420453f, 0.0883128f, 0.3594444f, -0.7174141f, 0.5683901f, 0.0096710f, -0.0957449f, -0.0195320f },\n",
+      "{ 0.1561092f, -0.0417566f, -0.1044470f, 0.1186895f, -0.1195878f, 0.0446987f, -0.1386125f, -0.0103878f, 0.1173026f, 0.1349312f, -0.0676422f, -0.1452308f, 0.0093872f, 0.0069650f, 0.1739093f, -0.1592752f, -0.1329019f, -0.0459163f, -0.1511888f, -0.0040456f, 0.0065862f, 0.0106182f, 0.0318060f, 0.1003269f, 0.0249398f, 0.1661194f, -0.0286407f, -0.1062361f, 0.0026465f, -0.0091479f, -0.1493473f, 0.0519762f },\n",
+      "{ -0.0702637f, 0.1154817f, -0.0680643f, 0.1447217f, 0.1394082f, -0.0691432f, 0.0939426f, 0.0483852f, 0.1437123f, -0.1085759f, 0.0333924f, -0.0683726f, 0.0707103f, -0.0723069f, 0.0124601f, -0.0309495f, -0.0308395f, -0.0695953f, -0.1078720f, 0.0858701f, -0.0773453f, 0.0477413f, 0.0615588f, 0.1656474f, 0.1718751f, -0.1125762f, 0.1753366f, -0.0557704f, 0.0921221f, 0.0372290f, -0.1084552f, -0.0438967f },\n",
+      "{ -0.0557757f, 0.0694144f, 0.1150911f, -0.0202319f, 0.0661389f, -0.0928373f, 0.0441888f, -0.0028318f, -0.0039446f, 0.0294675f, 0.1353384f, 0.0427515f, 0.0695194f, 0.1329748f, 0.1339706f, 0.0713900f, -0.1384726f, 0.0925476f, 0.1581103f, 0.0100842f, -0.1248652f, -0.0173615f, 0.1637451f, -0.0025173f, -0.0331219f, -0.0335269f, 0.0949441f, 0.0538645f, 0.0834281f, 0.0137191f, -0.1360130f, 0.0074489f },\n",
+      "{ -0.0949665f, -0.2181539f, 0.0871969f, 3.0772011f, -0.1152011f, -0.0022047f, 1.2700632f, -0.1173392f, -0.1678371f, -1.3448639f, -0.2893313f, 1.5105180f, -0.6029126f, -1.1568675f, 1.4823192f, 0.1635401f, -2.2136483f, -1.4164798f, -0.4795305f, -0.0807557f, -1.6675406f, -0.0992591f, 2.1212378f, -0.9400231f, -0.5339298f, -0.0342672f, -2.3564072f, 1.3407421f, -3.8635128f, -0.1171367f, -0.0364181f, -3.2491686f },\n",
+      "{ -0.1047117f, -0.0540412f, -0.1137928f, 0.1582367f, -0.0982449f, 0.0511854f, -0.0805884f, -0.1141258f, 0.0931992f, -0.0227052f, 0.0780590f, -0.1288135f, -0.1186576f, -0.0754066f, -0.1234059f, -0.0091936f, 0.0205475f, 0.1640417f, -0.1527465f, 0.0068472f, -0.1239804f, -0.0448335f, -0.0061169f, -0.0078998f, 0.0253047f, 0.0712901f, 0.0024753f, -0.0259875f, -0.1238613f, 0.1096537f, -0.0953007f, 0.1385384f },\n",
+      "{ 0.0521762f, 1.4885306f, -0.1298001f, 2.3033395f, -0.1589162f, -0.8458843f, 0.0631668f, -0.1424429f, -0.0384785f, 0.5599840f, 0.0008631f, -1.5839294f, 1.9202064f, 0.6930331f, 0.4948464f, -0.6195241f, -3.0526664f, 3.1423819f, -1.3433597f, -0.1167206f, -1.3491610f, -0.0901343f, -1.2291449f, 3.5039587f, 0.4674770f, -0.3027362f, 0.8279622f, 0.3417586f, 0.1367343f, -0.1085793f, -0.1048759f, 1.2729272f },\n",
+      "{ -0.0029521f, 0.2439991f, -0.0858953f, -2.7804739f, -0.0220416f, 0.0256599f, -0.3304259f, -0.0586597f, -0.0459698f, 0.1670698f, -0.1359344f, -0.3957845f, -1.6954739f, 0.3318155f, 0.9375985f, 0.5211958f, 0.6071047f, -3.4249072f, 1.3199407f, 0.0136374f, 1.2692807f, 0.0233104f, -0.0731508f, 2.2171400f, -0.6052189f, -0.0698463f, 1.6376522f, -1.1908000f, -0.1706121f, -0.0380146f, 0.0144418f, 1.5177792f },\n",
+      "{ -0.0314772f, 0.0523589f, -0.0517322f, -0.0100344f, 0.0714635f, -0.1646974f, 0.0800682f, 0.1132821f, -0.0028872f, -0.1239987f, -0.1322138f, -0.1059789f, 0.1752418f, 0.0475279f, -0.0046871f, 0.1574167f, -0.0231106f, -0.0261228f, 0.0236005f, 0.1663371f, 0.1059707f, 0.1229704f, 0.1427562f, -0.1648343f, 0.0992667f, -0.0631751f, -0.1411413f, -0.0999486f, -0.0972435f, -0.1422556f, 0.0973614f, -0.0156000f },\n",
+      "{ -0.1309903f, -0.5060971f, -0.1911870f, 2.2349114f, 0.1010354f, 0.5538697f, 1.8757060f, -0.1538645f, -0.2073075f, -1.8350753f, 0.0532570f, 1.8151909f, -0.6800886f, 0.2615838f, -0.6204563f, -0.1238837f, -0.4772464f, -2.4070835f, -0.2783994f, -0.0211087f, -4.4925098f, -0.0790045f, 1.3566529f, -0.3650998f, -0.4658130f, -0.0479139f, -1.9361999f, 2.1485121f, -3.1108823f, -0.0020647f, -0.0489678f, -0.4781263f },\n",
+      "{ -0.0099352f, -1.9572417f, 0.0918592f, 0.7327217f, -0.0609625f, -0.1969659f, 0.1922992f, -0.1091586f, -0.2125459f, -1.9542989f, -0.1648019f, -0.9355955f, 0.9144324f, -5.0530005f, -0.2265045f, -0.5638458f, 4.4370432f, -2.0318019f, -1.5679311f, 0.0221776f, -0.4063498f, -0.1160609f, 0.9651156f, -0.2401051f, 0.1903293f, -0.2355373f, 0.2334733f, 0.1025979f, 0.7150746f, 0.0315593f, -0.0001765f, 0.0137871f },\n",
+      "{ 0.0320691f, -1.8876421f, -0.1241799f, -3.1652985f, -0.1528286f, 2.1882250f, -2.5907574f, 0.0210803f, -0.1545521f, 0.7706368f, -0.1652040f, -4.1518817f, 4.2974262f, 0.3074523f, 3.3711803f, -37.9055862f, 1.0623894f, 0.4360786f, -2.6417589f, 0.1113010f, 3.8902094f, -0.1616735f, 0.5595753f, 1.5364015f, -2.4740698f, -0.0240434f, -28.0232792f, 0.6092473f, 1.6978041f, -0.0458809f, 0.0664777f, 0.2603019f },\n",
+      "{ 0.1044999f, 0.0054908f, 0.1407564f, -0.1701076f, -0.1274551f, 0.0443607f, 0.1182709f, -0.1103420f, -0.1343671f, -0.0042888f, -0.1611361f, 0.0154269f, 0.2285106f, 0.0870507f, 0.0914433f, 0.0657276f, -0.1664300f, -0.0342912f, 0.1037545f, -0.1175308f, 0.1135652f, 0.1325845f, -0.1459545f, -0.2156865f, -0.1673723f, -0.1156510f, 0.0179541f, 0.0541515f, 0.0957617f, -0.1297485f, 0.1045326f, 0.2950188f },\n",
+      "{ -0.1401742f, -2.8181052f, -0.0588381f, -0.1517100f, -0.0608850f, -3.5837226f, -0.1528927f, -0.0211265f, 0.0881796f, -0.4448619f, -0.1457623f, -0.8828475f, 0.1261238f, -1.0495204f, -3.7918513f, -0.4645159f, -0.0800092f, 0.0624971f, 0.1528609f, -0.1069645f, 0.4319421f, 0.0651448f, -0.6571375f, -0.0323338f, -4.6534319f, -0.0538999f, -0.2221518f, 0.0972160f, 0.1496329f, 0.0570569f, -0.1125795f, -0.0153687f },\n",
+      "{ -0.1065502f, 0.0606179f, -0.1400291f, -0.0220975f, -0.0613350f, -0.0038843f, -0.0132201f, 0.1678067f, 0.1008587f, -0.1255144f, -0.0675021f, -0.0475353f, 0.0278098f, 0.0527470f, -0.0089845f, -0.0622052f, 0.1088723f, 0.0053812f, 0.0627310f, -0.0226460f, -0.1096366f, -0.0505830f, -0.0301058f, -0.0775778f, -0.0008928f, -0.1157909f, 0.0544982f, 0.0430219f, -0.0134386f, -0.1095094f, 0.1215172f, 0.0081556f },\n",
+      "{ -0.1747307f, -0.7465636f, -0.0497346f, -2.0686443f, 0.0190713f, -2.9156351f, -5.4731860f, -0.0728399f, -0.0845178f, -14.8429976f, -0.1068359f, 1.8549156f, -3.1135283f, -0.0907917f, -0.0262453f, -8.8010912f, -4.3007965f, -1.6772208f, -0.2576891f, -0.0163111f, -7.8583646f, 0.0697906f, -0.0943863f, -0.7450574f, 1.1493169f, 0.0921000f, -0.2395420f, 0.5794312f, -4.2405462f, -0.0910322f, -0.1381017f, -1.0270567f },\n",
+      "{ -0.0446755f, -0.8131990f, -0.1741483f, -1.7555307f, 0.0153283f, 0.0734032f, -0.5930048f, -0.0398877f, -0.0215982f, 0.0497884f, -0.0504920f, 0.0942539f, -1.1370168f, -0.8821361f, -0.0879569f, 0.3811991f, 1.2224945f, 0.3782545f, 1.4800016f, 0.0494110f, 1.7101970f, -0.2885793f, -0.1778114f, -1.3913733f, -0.0944610f, -0.3578439f, 0.3491475f, -3.0349872f, 0.8044587f, 0.0928676f, -0.0395946f, 0.2008810f },\n",
+      "{ 0.0721043f, -0.1181163f, 0.0108281f, -0.1215726f, 0.1285277f, 0.0851443f, 0.0791321f, 0.1765833f, -0.0324889f, -0.0150838f, -0.0051942f, 0.1685798f, 0.1521861f, 0.0283858f, 0.0326072f, 0.0346215f, -0.1081120f, -0.0745824f, -0.1762613f, 0.0901582f, 0.1335704f, 0.1599123f, -0.0097813f, 0.0364541f, -0.0391450f, -0.0079635f, 0.1014886f, 0.0130333f, 0.0438304f, -0.0074333f, 0.0845035f, -0.0471010f },\n",
+      "{ 0.0360538f, -0.9701002f, -0.2217611f, -1.1626705f, 0.0548465f, 0.6605385f, -0.6693703f, -0.1432099f, -0.0754442f, -0.2380328f, -0.0754142f, -2.3242903f, 3.5773275f, 0.0707042f, 0.2052065f, -1.3753067f, -0.8530636f, 3.1850073f, -0.2901604f, -0.1291050f, -4.4672642f, -0.2425279f, 0.1252670f, 0.4261391f, -0.8620862f, 0.1153403f, -0.1999598f, -4.7756801f, 2.8851914f, -0.1340472f, 0.0482952f, 1.7996837f },\n",
+      "{ -0.1654812f, 0.9604513f, 0.1770310f, -16.5736618f, -0.0350192f, -0.5557595f, -35.3047371f, -0.1299658f, 0.0065243f, -3.0823336f, 0.0351931f, 4.9456911f, -1.4382623f, -1.6900688f, -1.9084880f, -3.1811504f, -8.0212736f, -7.3994560f, 4.9219728f, 0.0433824f, 0.6197430f, 0.0308996f, 5.2004323f, 0.5327767f, 1.0885966f, 0.1487215f, -21.4211712f, -1.8733859f, 1.9195696f, -0.0539309f, -0.0795544f, -3.1121061f },\n",
+      "{ -0.0058153f, 1.7521383f, -0.2205407f, 2.6318321f, -0.0038140f, -1.4131194f, 3.0181022f, 0.0373498f, -0.1246315f, -1.8323456f, -0.1470954f, 2.9131169f, 1.1522563f, 0.6036215f, -3.3962972f, 7.0906253f, -1.5353408f, -0.2648884f, 0.5501783f, -0.2262681f, -2.4874980f, -0.0533402f, 3.0222948f, 0.3296265f, 1.4057258f, 0.0185255f, 6.1208682f, 0.7210779f, -0.3055671f, -0.2595702f, -0.1286864f, 0.6510819f },\n",
+      "{ -0.2145578f, 0.4758183f, -0.1186396f, -0.6096930f, -0.1574199f, -0.1929667f, -0.6877209f, -0.2098342f, 0.0726678f, 0.1379885f, 0.0710437f, -1.1860796f, 0.6582619f, 0.2388466f, 0.0458675f, -0.0634391f, -0.1678368f, -8.2454395f, -0.6461441f, -0.2063597f, 0.0304686f, 0.0319904f, -1.0730971f, 1.1281222f, 0.1292592f, -0.3054110f, 0.7732272f, -1.0069786f, -0.0847367f, -0.2342585f, -0.1553642f, 1.5100089f },\n",
+      "{ -0.1022291f, 2.7367072f, -0.1738961f, -1.0328600f, -0.0864617f, -0.3224345f, -2.6092832f, -0.2382921f, 0.0578183f, 0.4115438f, 0.0121692f, -1.0689495f, 0.5158959f, 2.9600139f, 0.8839240f, -0.7147520f, -2.7168157f, 1.2148006f, 1.5884653f, -0.1227511f, 1.3176637f, -0.1335970f, -1.4691980f, 1.1131358f, -0.1302031f, 0.0779746f, 0.2622980f, 0.0837635f, 2.7756395f, -0.0315265f, 0.0868374f, -4.2980185f },\n",
+      "{ 0.0228074f, 2.1787968f, -0.1889012f, -0.8560471f, -0.1063542f, -0.2869910f, 0.2767612f, -0.1183861f, -0.0992468f, 2.1517978f, -0.0428540f, 1.0697522f, 1.9683092f, 2.1042306f, -0.0426359f, -0.3499008f, -0.9989156f, 0.0880459f, 2.9753070f, -0.1941337f, -3.1616704f, -0.0093505f, 1.4922180f, 2.8480091f, 0.2656264f, -0.1299839f, -1.0458518f, -1.6748481f, -3.1420829f, -0.1360553f, -0.1117443f, -1.3989290f },\n",
+      "{ -0.0246332f, 0.1165779f, 0.0255498f, -0.0601489f, 0.1545041f, -0.0977981f, 0.1242626f, -0.1533627f, -0.1294386f, -0.0231293f, -0.1460808f, 0.1763088f, 0.0953614f, -0.0716483f, -0.1003436f, 0.0804519f, 0.1373295f, -0.0686773f, 0.1198382f, 0.1519430f, 0.1640775f, -0.1675753f, 0.0790529f, -0.1521838f, 0.0378523f, 0.1039687f, -0.0701027f, 0.0509319f, 0.1355647f, 0.0978021f, 0.0391430f, 0.0241266f },\n",
+      "};\n",
+      "\n",
+      "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_output_layer[1] = {\n",
+      "-0.7420582f };\n",
+      "\n",
+      "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_output_layer[32][1] = {\n",
+      "{ 0.0381968f },\n",
+      "{ 1.0667214f },\n",
+      "{ 0.0505496f },\n",
+      "{ -1.5677565f },\n",
+      "{ 0.0066824f },\n",
+      "{ -0.9951485f },\n",
+      "{ 0.9438043f },\n",
+      "{ 0.0068631f },\n",
+      "{ -0.0216870f },\n",
+      "{ 0.6560486f },\n",
+      "{ -0.0235629f },\n",
+      "{ 0.9653404f },\n",
+      "{ 0.6641668f },\n",
+      "{ -0.5351945f },\n",
+      "{ -0.5303048f },\n",
+      "{ 1.9339687f },\n",
+      "{ 0.4359012f },\n",
+      "{ -0.7492802f },\n",
+      "{ -0.5728400f },\n",
+      "{ 0.0473893f },\n",
+      "{ -0.5091293f },\n",
+      "{ -0.1926489f },\n",
+      "{ -0.6562935f },\n",
+      "{ -0.5583456f },\n",
+      "{ -0.7618014f },\n",
+      "{ -0.0316967f },\n",
+      "{ 1.1637378f },\n",
+      "{ -0.5158406f },\n",
+      "{ -0.5268564f },\n",
+      "{ 0.0735416f },\n",
+      "{ 0.0270067f },\n",
+      "{ -0.5614370f },\n",
+      "};\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_formatted_weights_biases(weights, biases, layer_name):\n",
+    "    # Print biases\n",
+    "    print(f\"ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_{layer_name}[{len(biases)}] = {{\")\n",
+    "    print(\", \".join(f\"{b:.7f}f\" for b in biases) + \" };\")\n",
+    "    print()\n",
+    "\n",
+    "    # Print weights\n",
+    "    print(f\"ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_{layer_name}[{len(weights[0])}][{len(weights)}] = {{\")\n",
+    "    for row in weights.T:\n",
+    "        formatted_row = \", \".join(f\"{w:.7f}f\" for w in row)\n",
+    "        print(f\"{{ {formatted_row} }},\")\n",
+    "    print(\"};\")\n",
+    "    print()\n",
+    "\n",
+    "def print_model_weights_biases(model):\n",
+    "    # Make sure the model is in evaluation mode\n",
+    "    model.eval()\n",
+    "\n",
+    "    # Iterate through all named modules in the model\n",
+    "    for name, module in model.named_modules():\n",
+    "        # Check if the module is a linear layer\n",
+    "        if isinstance(module, nn.Linear):\n",
+    "            # Get weights and biases\n",
+    "            weights = module.weight.data.cpu().numpy()\n",
+    "            biases = module.bias.data.cpu().numpy()\n",
+    "\n",
+    "            # Print formatted weights and biases\n",
+    "            print_formatted_weights_biases(weights, biases, name.replace('.', '_'))\n",
+    "\n",
+    "print_model_weights_biases(model)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ensure input_features_tensor is moved to the appropriate device\n",
+    "input_features_tensor = input_features_tensor.to(device)\n",
+    "\n",
+    "# Make predictions\n",
+    "with torch.no_grad():\n",
+    "    model.eval()\n",
+    "    outputs = model(input_features_tensor)\n",
+    "    predictions = outputs.squeeze().cpu().numpy()\n",
+    "\n",
+    "full_tracks = (np.concatenate(branches['t5_isFake']) == 0) * (np.concatenate(branches['t5_pMatched']) > 0.95)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "pt: 0 to 5\n",
+      "93% Retention Cut: {0.7831, 0.8153, 0.8313, 0.823, 0.7426, 0.7532, 0.8392, 0.8636, 0.9172, 0.9389} Mean: 0.8307\n",
+      "98% Retention Cut: {0.4493, 0.4939, 0.5715, 0.6488, 0.5709, 0.5938, 0.7164, 0.7565, 0.8103, 0.8593} Mean: 0.6471\n",
+      "99% Retention Cut: {0.2946, 0.3312, 0.4081, 0.5213, 0.4509, 0.495, 0.6333, 0.6726, 0.7225, 0.7661} Mean: 0.5295\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "pt: 5 to inf\n",
+      "93% Retention Cut: {0.6982, 0.7335, 0.7395, 0.8015, 0.7356, 0.6149, 0.6848, 0.6468, 0.7187, 0.7079} Mean: 0.7081\n",
+      "98% Retention Cut: {0.4488, 0.4448, 0.5067, 0.5929, 0.4836, 0.4112, 0.4968, 0.4403, 0.5597, 0.5067} Mean: 0.4891\n",
+      "99% Retention Cut: {0.3302, 0.3319, 0.3761, 0.4848, 0.3578, 0.2981, 0.3546, 0.3146, 0.4669, 0.4086} Mean: 0.3724\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.colors import LogNorm\n",
+    "\n",
+    "def plot_for_pt_bin(pt_min, pt_max, percentiles, eta_bin_edges, eta_list, predictions, full_tracks, branches):\n",
+    "    \"\"\"\n",
+    "    Calculate and plot cut values for specified percentiles in a given pt bin\n",
+    "    \n",
+    "    Parameters:\n",
+    "    -----------\n",
+    "    pt_min : float\n",
+    "        Minimum pt value for the bin\n",
+    "    pt_max : float\n",
+    "        Maximum pt value for the bin\n",
+    "    percentiles : list\n",
+    "        List of percentiles to calculate (e.g., [92.5, 96.7, 99])\n",
+    "    eta_bin_edges : array\n",
+    "        Edges of the eta bins\n",
+    "    eta_list : list\n",
+    "        List of eta values\n",
+    "    predictions : array\n",
+    "        Array of DNN predictions\n",
+    "    full_tracks : array\n",
+    "        Boolean array for track selection\n",
+    "    branches : dict\n",
+    "        Dictionary containing branch data\n",
+    "    \"\"\"\n",
+    "    # Filter data based on pt bin\n",
+    "    abs_eta = eta_list[0][full_tracks & (np.concatenate(branches['t5_pt']) > pt_min) & \n",
+    "                         (np.concatenate(branches['t5_pt']) <= pt_max)]\n",
+    "    predictions_filtered = predictions[full_tracks & (np.concatenate(branches['t5_pt']) > pt_min) & \n",
+    "                                    (np.concatenate(branches['t5_pt']) <= pt_max)]\n",
+    "    \n",
+    "    # Dictionary to store cut values for different percentiles\n",
+    "    cut_values = {p: [] for p in percentiles}\n",
+    "\n",
+    "    # Loop through each eta bin\n",
+    "    for i in range(len(eta_bin_edges) - 1):\n",
+    "        # Get indices of tracks within the current eta bin\n",
+    "        bin_indices = (abs_eta >= eta_bin_edges[i]) & (abs_eta < eta_bin_edges[i + 1])\n",
+    "        \n",
+    "        # Get the corresponding DNN prediction scores\n",
+    "        bin_predictions = predictions_filtered[bin_indices]\n",
+    "        \n",
+    "        # Calculate the percentile cut values for the current bin\n",
+    "        for percentile in percentiles:\n",
+    "            cut_value = np.percentile(bin_predictions, 100 - percentile)  # Convert retention to percentile\n",
+    "            cut_values[percentile].append(cut_value)\n",
+    "\n",
+    "    # Plot 2D histogram\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.hist2d(abs_eta, predictions_filtered, bins=[eta_bin_edges, 50], norm=LogNorm())\n",
+    "    plt.colorbar(label='Counts')\n",
+    "    plt.xlabel(\"Absolute Eta\")\n",
+    "    plt.ylabel(\"DNN Prediction Score\")\n",
+    "    plt.title(f\"DNN Score vs. Abs Eta for 100% Matched Tracks (pt: {pt_min} to {pt_max})\")\n",
+    "\n",
+    "    # Plot the cut values with different colors\n",
+    "    cut_x = eta_bin_edges[:-1] + (eta_bin_edges[1] - eta_bin_edges[0]) / 2  # Mid-points of the bins\n",
+    "    colors = plt.cm.rainbow(np.linspace(0, 1, len(percentiles)))  # Generate distinct colors\n",
+    "    \n",
+    "    for percentile, color in zip(percentiles, colors):\n",
+    "        plt.plot(cut_x, cut_values[percentile], '-', color=color, marker='o', \n",
+    "                label=f'{percentile}% Retention Cut')\n",
+    "    \n",
+    "    plt.legend()\n",
+    "    plt.grid(True, alpha=0.3)\n",
+    "    plt.show()\n",
+    "    \n",
+    "    # Print the cut values\n",
+    "    print(f\"\\npt: {pt_min} to {pt_max}\")\n",
+    "    for percentile in percentiles:\n",
+    "        values = cut_values[percentile]\n",
+    "        print(f\"{percentile}% Retention Cut:\", \n",
+    "              '{' + ', '.join(str(x) for x in np.round(values, 4)) + '}',\n",
+    "              \"Mean:\", np.round(np.mean(values), 4))\n",
+    "\n",
+    "# Example usage:\n",
+    "def analyze_pt_bins(pt_bins, percentiles, eta_bin_edges, eta_list, predictions, full_tracks, branches):\n",
+    "    \"\"\"\n",
+    "    Analyze and plot for multiple pt bins and percentiles\n",
+    "    \n",
+    "    Parameters:\n",
+    "    -----------\n",
+    "    pt_bins : list\n",
+    "        List of pt bin edges\n",
+    "    percentiles : list\n",
+    "        List of percentiles to calculate\n",
+    "    Other parameters same as plot_for_pt_bin function\n",
+    "    \"\"\"\n",
+    "    for i in range(len(pt_bins) - 1):\n",
+    "        plot_for_pt_bin(pt_bins[i], pt_bins[i + 1], percentiles, eta_bin_edges, \n",
+    "                       eta_list, predictions, full_tracks, branches)\n",
+    "\n",
+    "# Example call:\n",
+    "percentiles = [93, 98, 99]\n",
+    "pt_bins = [0, 5, np.inf]\n",
+    "eta_bin_edges = np.arange(0, 2.75, 0.25)\n",
+    "analyze_pt_bins(pt_bins, percentiles, eta_bin_edges, eta_list, predictions, full_tracks, branches)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "analysisenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/RecoTracker/LSTCore/standalone/analysis/occupancy/compute_occupancies.ipynb b/RecoTracker/LSTCore/standalone/analysis/occupancy/compute_occupancies.ipynb
new file mode 100644
index 0000000000000..246d12b11e8c5
--- /dev/null
+++ b/RecoTracker/LSTCore/standalone/analysis/occupancy/compute_occupancies.ipynb
@@ -0,0 +1,586 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uproot\n",
+    "import numpy as np\n",
+    "\n",
+    "def load_root_file(file_path, branches=None, print_branches=False):\n",
+    "    all_branches = {}\n",
+    "    with uproot.open(file_path) as file:\n",
+    "        tree = file[\"tree\"]\n",
+    "        # Load all ROOT branches into array if not specified\n",
+    "        if branches is None:\n",
+    "            branches = tree.keys()\n",
+    "        # Option to print the branch names\n",
+    "        if print_branches:\n",
+    "            print(\"Branches:\", branches)\n",
+    "        # Each branch is added to the dictionary\n",
+    "        for branch in branches:\n",
+    "            all_branches[branch] = tree[branch].array(library=\"np\")\n",
+    "    return all_branches\n",
+    "\n",
+    "# Branches relevant to the occupancy selections\n",
+    "mod_occ_branches = ['module_layers', 'module_subdets', 'module_rings', 'module_eta',\n",
+    "                    'md_occupancies', 'sg_occupancies', 't3_occupancies', 't5_occupancies']\n",
+    "\n",
+    "# Root file generated with compile -d option turned on to generate relevant occupancy branches\n",
+    "file_path = \"occ_1000_p06.root\"\n",
+    "branches = load_root_file(file_path, mod_occ_branches)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "events = np.shape(branches['module_layers'])[0]\n",
+    "\n",
+    "module_layers = np.concatenate(branches['module_layers'])\n",
+    "module_subdets = np.concatenate(branches['module_subdets'])\n",
+    "module_rings = np.concatenate(branches['module_rings'])\n",
+    "module_eta = np.abs(np.concatenate(branches['module_eta']))\n",
+    "\n",
+    "category_numbers = np.full_like(module_layers, -1)\n",
+    "\n",
+    "# Different category masks\n",
+    "mask1 = (module_layers <= 3) & (module_subdets == 5)\n",
+    "mask2 = (module_layers >= 4) & (module_subdets == 5)\n",
+    "mask3 = (module_layers <= 2) & (module_subdets == 4) & (module_rings >= 11)\n",
+    "mask4 = (module_layers >= 3) & (module_subdets == 4) & (module_rings >= 8)\n",
+    "mask5 = (module_layers <= 2) & (module_subdets == 4) & (module_rings <= 10)\n",
+    "mask6 = (module_layers >= 3) & (module_subdets == 4) & (module_rings <= 7)\n",
+    "\n",
+    "category_numbers[mask1] = 0\n",
+    "category_numbers[mask2] = 1\n",
+    "category_numbers[mask3 | mask4] = 2\n",
+    "category_numbers[mask5 | mask6] = 3\n",
+    "\n",
+    "eta_numbers = np.full_like(module_eta, -1)\n",
+    "\n",
+    "# Different eta masks\n",
+    "eta_numbers[module_eta < 0.75] = 0\n",
+    "eta_numbers[(module_eta >= 0.75) & (module_eta < 1.5)] = 1\n",
+    "eta_numbers[(module_eta >= 1.5) & (module_eta < 2.25)] = 2\n",
+    "eta_numbers[(module_eta >= 2.25) & (module_eta < 3)] = 3\n",
+    "\n",
+    "# Split the arrays back into event-wise lists\n",
+    "split_indices = np.cumsum([len(x) for x in branches['module_layers'][:-1]])\n",
+    "\n",
+    "category_numbers_split = np.split(category_numbers, split_indices)\n",
+    "eta_numbers_split = np.split(eta_numbers, split_indices)\n",
+    "\n",
+    "# Add category number and eta number branches\n",
+    "branches['category_number'] = np.array(category_numbers_split, dtype=object)\n",
+    "branches['eta_number'] = np.array(eta_numbers_split, dtype=object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "font = {'size' : 20}\n",
+    "\n",
+    "matplotlib.rc('font', **font)\n",
+    "\n",
+    "def plot_histogram(data, title, xlabel, ylabel, occ_percentile=None):\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.hist(data, bins=50, edgecolor='black', alpha=0.7)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(xlabel)\n",
+    "    plt.ylabel(ylabel)\n",
+    "    plt.grid(True)\n",
+    "    plt.yscale('log')\n",
+    "    # Plotting a vertical line at the occupancy value\n",
+    "    if occ_percentile is not None:\n",
+    "        non_zero_data = data[data > 0]\n",
+    "        percentile_value = np.percentile(non_zero_data, occ_percentile)\n",
+    "        plt.axvline(percentile_value, color='red', linestyle='dashed', linewidth=1, label=f'{occ_percentile}th percentile: {percentile_value:.0f}')\n",
+    "        plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_occupancies(branches, occupancy_variables, occ_percentiles, plot=False):\n",
+    "    cat_eta_combinations = [(cat, eta) for cat in range(4) for eta in range(4)]\n",
+    "\n",
+    "    for var, percentile in zip(occupancy_variables, occ_percentiles):\n",
+    "        for cat, eta in cat_eta_combinations:\n",
+    "            data_to_plot = [\n",
+    "                occupancy for sublist_cat, sublist_eta, sublist_occ in zip(branches['category_number'], branches['eta_number'], branches[var])\n",
+    "                for c, e, occupancy in zip(sublist_cat, sublist_eta, sublist_occ) if c == cat and e == eta\n",
+    "            ]\n",
+    "            data_to_plot = np.array(data_to_plot)\n",
+    "            non_zero_data = data_to_plot[data_to_plot > 0]\n",
+    "            if non_zero_data.any():\n",
+    "                if plot:\n",
+    "                    plot_histogram(data_to_plot, f'{var} for Category {cat} and Eta {eta}', 'Occupancy', 'Frequency', percentile)\n",
+    "                else:\n",
+    "                    percentile_value = np.percentile(non_zero_data, percentile)\n",
+    "                    print(f'{var} for Category {cat} and Eta {eta} - {percentile}th percentile: {percentile_value:.0f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "md_occupancies for Category 0 and Eta 0 - 99.99th percentile: 60\n",
+      "md_occupancies for Category 0 and Eta 1 - 99.99th percentile: 57\n",
+      "md_occupancies for Category 0 and Eta 2 - 99.99th percentile: 54\n",
+      "md_occupancies for Category 0 and Eta 3 - 99.99th percentile: 48\n",
+      "md_occupancies for Category 1 and Eta 0 - 99.99th percentile: 259\n",
+      "md_occupancies for Category 1 and Eta 1 - 99.99th percentile: 195\n",
+      "md_occupancies for Category 2 and Eta 1 - 99.99th percentile: 23\n",
+      "md_occupancies for Category 2 and Eta 2 - 99.99th percentile: 28\n",
+      "md_occupancies for Category 3 and Eta 1 - 99.99th percentile: 25\n",
+      "md_occupancies for Category 3 and Eta 2 - 99.99th percentile: 25\n",
+      "md_occupancies for Category 3 and Eta 3 - 99.99th percentile: 33\n",
+      "sg_occupancies for Category 0 and Eta 0 - 99.9th percentile: 936\n",
+      "sg_occupancies for Category 0 and Eta 1 - 99.9th percentile: 351\n",
+      "sg_occupancies for Category 0 and Eta 2 - 99.9th percentile: 256\n",
+      "sg_occupancies for Category 0 and Eta 3 - 99.9th percentile: 61\n",
+      "sg_occupancies for Category 1 and Eta 0 - 99.9th percentile: 1358\n",
+      "sg_occupancies for Category 1 and Eta 1 - 99.9th percentile: 763\n",
+      "sg_occupancies for Category 2 and Eta 1 - 99.9th percentile: 210\n",
+      "sg_occupancies for Category 2 and Eta 2 - 99.9th percentile: 268\n",
+      "sg_occupancies for Category 3 and Eta 1 - 99.9th percentile: 60\n",
+      "sg_occupancies for Category 3 and Eta 2 - 99.9th percentile: 97\n",
+      "sg_occupancies for Category 3 and Eta 3 - 99.9th percentile: 96\n",
+      "t3_occupancies for Category 0 and Eta 0 - 99.9th percentile: 1146\n",
+      "t3_occupancies for Category 0 and Eta 1 - 99.9th percentile: 544\n",
+      "t3_occupancies for Category 0 and Eta 2 - 99.9th percentile: 216\n",
+      "t3_occupancies for Category 0 and Eta 3 - 99.9th percentile: 83\n",
+      "t3_occupancies for Category 1 and Eta 0 - 99.9th percentile: 1032\n",
+      "t3_occupancies for Category 1 and Eta 1 - 99.9th percentile: 275\n",
+      "t3_occupancies for Category 3 and Eta 1 - 99.9th percentile: 115\n",
+      "t3_occupancies for Category 3 and Eta 2 - 99.9th percentile: 110\n",
+      "t3_occupancies for Category 3 and Eta 3 - 99.9th percentile: 76\n",
+      "t5_occupancies for Category 0 and Eta 0 - 99.99th percentile: 325\n",
+      "t5_occupancies for Category 0 and Eta 1 - 99.99th percentile: 237\n",
+      "t5_occupancies for Category 0 and Eta 2 - 99.99th percentile: 217\n",
+      "t5_occupancies for Category 0 and Eta 3 - 99.99th percentile: 176\n",
+      "t5_occupancies for Category 3 and Eta 2 - 99.99th percentile: 129\n",
+      "t5_occupancies for Category 3 and Eta 3 - 99.99th percentile: 180\n"
+     ]
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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BG80cH/WSfypbZWyOD/qY/wCtNH+tH/XWT+VLbdR/1xj/AK0ACo3mLwfvS/zpio3lrwfuQf8AoVOT/WL/AL0v86Yv+rX/AHIP/QqAH7W3dD1mr0Pwn4na7jFpqVzuuS0YhXyscY9QMdu9edfxfjNU1q7Ry70ZlYJHhlOCOD3rOrTVSNmaUqrpu6Pb55U8iT5h90/ypUlTy1+YdBXmPh/xZNYvJHqNzdT25jlWNOGwQ3HJ56Z716jHzGp9hXm1Kbpysz1adWNSF0J5sf8AeFMklTC/MPvCpqZJ0X/eFQUHmx/3hR5sf94U+igBnmx/3hTXlT5fmH3hUtMk/h/3hQBXuZFZ7YBgT5w/kaKdd/ftv+uw/kaKALNIWUEgsMgZPPalrn9Y0S51DUmnhW1WNrXyWLk7nIkVwrADlflIPP8AEePUA07+WKS0uIkdGdFVmUNyATwf0P5UzXf+QBf/APXB/wCVUE0k6dosmZT55RUbYTsUbywVR6DcR9Ku64mNCvzuY/uH4J9qcd0TLZnjI/4+B/18f+06hH/HuP8Argn/AKFU4b/SB8o/1/8A7TqEN/o4+Vf9Qnb/AGq9g8YlH/HwP+u7f+gUlr0T/rhH/WnBv9IHA/17f+gUWzcJ8o/1Efb60ANX/Wp/13f/ANBNRL/qF/64w/8AoVTq371flH+vf+RqJW/cL8q/6mLt/tUAPH+tH/XWT+VLbdR/1xj/AK0Bv3o+Uf6yT+VLbNyOB/qY/wCtADU/1i/70v8AOmL/AKtf9yD/ANCqRW/eL8o+9L296Yrfu1+VfuQdv9qgBf4vxmp8H3z/ANc4/wCtN3fN90dZu1SQN8x4H+rj/rQBEDgg+gmP/j1ep6V400+5sZZbwx2fk7QEaTcz56EADJ59q8s3cHgdJv8A0KnMfnPyjrD296yq0lU3N6VV01oe42d5Bf2kd1bPvhkztbBGcHHQ/SpJOi/7wrz3wBeXMuoy2z3MvkJFIVj3nauJB0Hbqa73KSKDHNvG4DKsDXn1IckuU9ClPnjzFiimeX/tv+dHl/7b/nWZoPpkn8P+8KPL/wBt/wA6a6fd+dvvetAEd39+2/67D+RoptymHtjuY/vh1PsaKALdFFc9rOs3VhqbW0MkRBtPNCmMkxnzFUscHkBWY4/2aANfUv8AkHzcgcDr9ah13/kAX/8A1wf+VZdvcanPp017dMrQSR4RFG3PzABgCMgEAt1PDD0rS1xmOhX4KEDyH5yPSnHdEy2Z4yP+Pgf9fH/tOoR/x7j/AK4J/wChVOAPtA+b/lv6f9M6hAH2cfN/ywTt/tV7FzxrEo/4+B/13b/0Ckteif8AXCP+tOAH2gfN/wAt27f7FJbAYT5h/qI+31ouFhF/1qf9d3/9BNRL/qF/64w/+hVOoHmr83/Ld+3+yaiUDyF+Yf6mLt/tUXCw8f60f9dZP5Utt1H/AFxj/rQAPNHzf8tJO3tS2wGR83/LGPt9aAGp/rF/3pf50xf9Wv8AuQf+hVIoHmL838Uvb3pigeWvzD7kHb/aoCwv8X4zU+D75/65x/1puBu+93m7U+ADefm/5Zx9vrRcLEXY/Sf/ANCpX++frD/6FRgYPzDpN2/2qcwG8/MOsPb3ovqVb3TofB11b2t3etcTxQq1tOqmRwoJ3jjmq+l6/cabpt1YWyCMyOjiZWwynA6DHt+tYhA2n5h92ft71NCB5j/N2Tt7Vm6cW231t+BSqSSSXS/4nqeg6/ZNodq17qlt9o2HzPNmUNkE9ea2F1Gxe2a5W8tzAhw0okG0H0JzjuK8MZV8pvmH+rm7f7VdBb6xbQ+GLzSmWQzy3CMrBflH3Ov5VzTw3VM6oYro0esJPDLAJ45UaEjcJFYFceuaqpqunXUyRW9/bSyFuESVWJ/AGsnRyR4FQBSR9lk5/wC+q4nwcAPFUJHJ3dP+2dYxpXUnfY2lWacVbc9Ru/v23/XYfyNFNuWYvbZQj98O49DRWJuW6KKKAI54UuIWikBKMMHBxWTrUSQ6eYybiY3DrAIzOUBLHHJwcD8K2qiuLeC7gaG4hSWJvvI6hgfwNAHM2HhHw9f2FtfR2cyrOizqGnfI3L9fQ4qf/hA/D+3b9kkxtC/69+g/GujVVRFRFCqowABgAUtX7Sfdkezh2Rzn/CDaBu3fZZc7t3+vfrjHrWVqnhzQdJlRTp8xh+zSSGRLp9wEQ3bdp65Gec13FV5rC0ubiO4ntopJogQjugJUHggH3o9pPuw9nDsjl9M8KaFfwu72EkM0MzI6i6dwGHUg5GRg+lXP+ED8PgY+yy4wF/179B071v29tBaQLBbQxwxL0SNQoH4CpaPaT7sPZw7I5z/hBdAzn7LLnJP+vfqevesjV/D+haO6j+zpZI/JaRjHdvvVY1LcqeNvAXOerCu6rI1kWttJBdGxt5ruZ1tUklAAAbPDNgnb147k470e0n3Yezh2Rj6Z4T0G+t2lawkiljlkidRdOwDBiGwcjIOPSrf/AAgnh/GPssuMKP8AXv26d6bpviSzFgEt9OaNY5fs/lwlAhlJ6LkjjnO4gA59eKnuPFtlbts8id5MuCi7ARtMgOcsB/yyf9KPaT7sPZw7Ii/4QXQP+fWXv/y3fv171j61oWh6LLCDpk7xzDasi3Tj5lBIU54AOMdc9TjgmthvGmn75ljguZTEckoq4K7XYsCSOB5Tj1yMUs2raRcyxXFxpxkM7tawSPGjGU7tpUc5GctwccBqPaT7sPZw7Iqad4Q0C/sxObGSNt8sbp9pdgGDlWwc8jKnmrZ8C6ATn7LL2/5bv26d66CGGK3hSGGNI4kGFRBgAewp9HtJ92Hs4dkc3/wgnh//AJ9Zf4v+W79+vesHVdP8M6Zq8enJZyPcPGGIF4y/NlQi4JJwc8nsBXoVQz2ltdDFxBFL8pX50B4PUc+tHtJ92Hs4dkcvp3hDw9qGnpcfYZY929GT7S5wdxDDOeRkHmrf/CC6Bkn7LLkkH/Xv1HTvXQRRRwRJFDGscaDCoowAPQCn0e0n3Yezh2RnxaNawWQs4mnW3ClNnmt0PUfrXLS2GmaJreyDTp4pyyC1lN04E7MVQjkEADfz344FdzVWXTLG4kkkms4JHkXY7PGCWXjg/kPyFTzPuPlT6FfTfK1CxtL8NPiRBKqPITtJH69aK0ERIo1jjVURQAqqMAAdgKKRQ6iiigAooooAKKKKACiiigAooooAKZLDFcRNFNGkkbfeR1BB+oNFFAELafZOCGs7dgUEZzEpyo6L06D0qObSNOnkV5bG3Zg/mZMY5bBGT68MetFFAEosbNXdxawBpCS5EYyxORz69T+ZprabZtcRz/Z0EkchlBUYy5BXcQOpwSMn1oooAtUUUUAFFFFABRRRQAUUUUAFFFFAH//Z",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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gB9Mi/wBWKNzf88z+YpsTN5Y/dn8xQBLRTNzf88z+Yo3N/wA8z+YoAJf9U/8Aumn1FKzeU/7s/dPcU7c3/PM/mKAIbv79t/12H8jRTblmL22UI/fDuPQ0UAW6MUVz+sa7Pp2ptbRyWxBtfNCsDuQ+YqljzyoDFsYH3Tz6AGrqQzp0w9h/MVFrv/IAv/8Arg/8qzYb29u9KmurmMNBJEgTbgAsHYFgDzgjYwyTWhrjMdCvwUIHkPzkelOO6Jlszxkf8fA/6+P/AGnUI/49x/1wT/0KpwB9oHzD/X+n/TOoQB9nHzD/AFCdv9qvYueNYlH/AB8D/ru3/oFRp/qB/wBcYf8A0KpQB9oHzD/Xt2/2KjQDyB8w/wBTF2/2qGwSHj/Wp/12f/0E1Cv+oX/rjF/6FVgAeanzD/XP/wCgmoVA8hfmH+pi7f7VHQqW51/hPxQ1hcC0vrpY7BTIFBjyQc56gZ9a7e18RaPe3CLb6jA7sAFXdgknpgHrmvHQB5o+Yf6yTt7VoeHABrmn4Ofnt+PxNctWhF3kjoo4iStFntFFc5b+KC/iK502eOGGKINiRpMEkY9eO9bF5qEVhZSXk6sIYxuJXnj2ricWnZnepJq6LdMT70n+9/QVWsNRi1KyS7tlcxPnBYYPBwePwqdWbc/7s/e9R6CpasUnclopm5v+eZ/MUbm/55n8xQA+mRf6sUbm/wCeZ/MU2Jm8sfuz+YoAlopm5v8AnmfzFG5v+eZ/MUAEv+qf/dNPqKVm8p/3Z+6e4p25v+eZ/MUAQ3f37b/rsP5Gim3LMXtsoR++HcehooAt0UVz2s6zdWGptbQyREG080KYyTGfMVSxweQFZjj/AGaANbUxnTph7D+YqLXf+QBf/wDXB/5VlQXGpXGmzXt2Va3kQBEUbc/MAGAI4zgt1P3h6Vp64zHQr8FCB5D85HpTjuiZbM8ZH/HwP+vj/wBp1CP+Pcf9cE/9CqcAfaB83/Lf0/6Z1CAPs4+b/lgnb/ar2LnjWJR/x8D/AK7t/wCgVGn+oH/XGH/0KpQB9oHzf8t27f7FRoB5A+Yf6mLt/tUNgkPH+tT/AK7P/wCgmoV/1C/9cYf/AEKrAA81Pm/5bP2/2TUKgeQvzD/Uxdv9qhbFSWo8f60f9dZP5U1GZY1ZWKkLBgg4I+angDzR83/LSTt7U1QPJX5h92Dt/tUMlC5MkwLksS8uSxyetdtqPjHTLzww2nxJciZoIlBZAF5I759q4pQPMX5v4pe3vTFA8tfmH3IO3+1UTpxla/Q0hUlC9up6TpOt2ui+DLSS58w+Y0qqIxkg7mPrS/D6aSbT7tpJHf50I3sT/D715vtXdncM5m7V1fhTxNZaFZyxXKTO0hjIMagjkY7kVz1KNou2rbOilWvJX0SR6aSB1IFGRnGRmuB8ZX8WqaNpt3ErrFI0hXeOeBjt9Kl1kk+OtIJGCEiwP+BNXMqTa+/8DpdVJ/d+J3VMi/1Yo3N/zzP5imxM3lj92fzFZGxLSZGcZGfSm7m/55n8xXDal/yUi0bHzZj4/A1cI81yJz5bHcy/6p/900+opWbyn/dn7p7is6XxBbQ63HpLRTfaJACCANvIJ659qlJvYptLcu3f37b/AK7D+RoptyzF7bKEfvh3HoaKQy3RRRQBHNClxC0UgyjDBwcVk61EkOnmMm4mNw6wCMzlASxxycHA/Ctqori3gu4GhuIUlib7yOoYH8DQBzNh4R8PX9hbX0dnMqzos6hp3yNy/X0OKn/4QPw/t2/ZJMbQv+vfoPxro1VURURQqqMAAYAFLV+0n3ZHs4dkcjqvhLQ9O0y6v47CSZrdGm2G6dd2Bzzz2z2rIutK0CDUDZR6VMx8wW8RN44Dspj4brtUeapzyTg+2fQLm2gvLd7e5hSaGQYeORcqw9CKgbSdOYOGsbY+YgR8xD5lGMA+wwPyFHtJ92Hs4dkcZo+leHdXuo0TTJ40kRpY3a6cncFjLAjPH+tGDnnB6cZ2/wDhA/D4GPssuMBf9e/QdO9bsVjaQTtNDbQxysoQuiAEqOgz6cD8qno9pPuw9nDsjjdS8K6Hp/2dk095vNmCbRduHyxwSo53cZJ6YAJrFS08PMU/4lE4UxpMR9sbAhyu1v8Af+cfL9ea9EuLG1u5IZLi3ileE7omdQSh9Qe1Zupw2OnQ25i020aSW7Xy96hVWVv4ycHB469ScCj2k+7D2cOyMLRfD3h/Vlkb+zpYSoWRf9KdspIu4HrwcdR29a0/+EE8P4x9llxhR/r37dO9M0zxDYrayNbaYYibjynEGwK85PYkjIIwdxABHvxVi48W2Vu2zyJ3ky4KLsBG0yA5ywH/ACyf9KPaT7sPZw7Iwtb0LQdEkhL6VNLFKHAcXUgw+0nB7c49c+3BrPe18PwmTzNInPlv5LAXjfLKu/Cj1X92fmPr09OguPEmh3NzK76c91LEpUuYkOY9shYgsemI5AR17Y5qZdQ0QCBv7KUJKWs7dvJT95820oBnIByxwccBjR7Sfdh7OHZDrPQdL1XSII5reZY4JJYxD9oYhGV2VsHjIyDzV+bw7YT3sV5KJmuIgAjmZuMcjv7mtGGCK2hSGCNI4kGFRBgAewqSp5n3Hyx7HI3XiW2i1O7sLfzp5bfH/L3t3ELIz+pGBGR7k9utRf8ACSIqYjhu33B2hL3O0uqeZuLcfKf3TYHOcjpzjq7jT7O73fabSCbcAG8yMNnBJHX0yfzNRvpOnS+b5ljbN5rBpN0SneR0J457/nSKCK3SaFJVnudrqGGZDnBqrL4dsJb9b91na6TG2Tzmzx074rWoIyMHpTTa2E0nuca3iJRCRcW93C6KPtUYuDvg3FQoAKjcfmB9Mc5NQxXVjc61DcS2M63qPGkkouydm/aE29m/1gz0xz1rrI9J06IxGOwtlMTFoyIh8pPUj60selafCYTFY2yGEkxbYgNhPUjjjNCbWwNJ7jhZoJEdpJnKHcA0hIzRVmikMKKKKACiiigAooooAKKKKACiiigApksMVxE0U0aSRt95HUEH6g0UUAQtp9k4Iazt2BQRnMSnKjovToPSo5tI06eRXlsbdmD+ZkxjlsEZPrwx60UUASixs1d3FrAGkJLkRjLE5HPr1P5mmtptm1xHP9nQSRyGUFRjLkFdxA6nBIyfWiigC1RRRQAUUUUAFFFFABRRRQAUUUUAf//Z",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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kA3//AFwf+Rpx3QpbM8YUjzx8o/17d/8AYqIEfZvuj/j3j7+5qRf9eP8Aru3/AKBUI/49v+3eP+Zr2LHi3JwR9oHy/wDLdu/+xUSkeQvyj/Uxd/8AaqQf8fI/67t/6BUK/wCoX/rjF/6FQFywpHnfd/5ayd/9moVI8pflH+rh7/7VSr/rv+2sn/oNQr/ql/65wf8AoVFguTAjzh8v/LV+/wDs1EpHlL8o/wBXD3/2qeP9cP8Ars//AKDUa/6pf+uUP/oVFgueh2OtvoXg2yuEh83zJpE2ltuOWOc4PpXYxM0yQy7iN8e7GBxnFeI+bIcRmVzGHlwhY7R+HSui8Ja9qFvqNtZiXzIZmhVvNyxAOQcEnjpXHVw+jkvM7aOI1UX5HqW1v+eh/IUbW/56H8hT6K4juGbW/wCeh/IUbW/56H8hT6KAIgrea37w/dHYe9O2t/z0P5ClH+tb/dH9adQBXuVb7NJ+8PT0FS7W/wCeh/IU26/49pPpUtLqV9kZtb/nofyFNRWy/wC8P3vQVLTE6v8A71Mkr3asBBlyf3y8YHrRTrzpD/12T+dFAFmiisLVtdl02/a3CQMptvNTc5BDeYqZb/Z+fOfY0Aaeo/8AIPmyCfl7VDrn/IBv/wDrg/8AI1mQalf31jPczwoto0ZVDHzlg23IOckEhj06bfetHW3zoV+Nrf6h+3tTjuhS2Z4yv+vH/Xdv/QKhH/Ht/wBu8f8AM1OF/fjkf69u/wDsVEF/0b7y/wDHvH39zXsXPFsSD/j5H/Xdv/QKhX/UL/1xi/8AQqsBf9IHI/157/7FQqv7hfmX/Uxd/wDaouFiVf8AXf8AbWT/ANBqFf8AVL/1zg/9CqdV/fdR/rX7/wCzUKr+6X5l/wBXD3/2qLhYeP8AXD/rs/8A6DUa/wCqX/rlD/6FUwX98OR/rn7/AOzUSr+6X5l/1UPf/aouFh4+/wD8DlrS8Nf8h+w/66W/8zWcF+fqPvy96u6JPFZ6raXE7hYo2gZiOcDJqZ/CyqekkdSmvjS/Gt699c3Btt0ihAWYA8Y+X8DRc+I49W8XaY2n3NwLfMQdCWQHLnqO/FcxrVxFe63cXMDhopJZSpPGeKZok8Vnq1nczuFijMDMRzgZNY+xjy83W36G/tpc3L0v+p7ZVa/voNNspLu5LCKPG7aMnkgDj8ax/wDhN9A/5/G7/wDLJu3XtWV4j8VaPqOhXFrbXRaZzHtBjYfxA9SPauKNKTaTTO2VWKi2mjq7C9h1G3S7tyxikUFSwwepHT8Kt1xXh3xTpFho1tbXFyVlRWBAjY9HOeg9xXSS65YQ30NnJKwuJgDGmw8g9OfwpSg07WHComk7keu6zZ6PaKbtnAlyF2IW6fStQHIBHQ1w/wAR2BsLQkFQDIefoK6nTNXs9UtzJZu0qoQrHYRg4z3ocLRUkUp3bizQpidX/wB6jzP9hvyrPbWrGDUl0+SRlupCCqbDznpz07GpSb2G2luWrzpD/wBdk/nRTbt8iAbWH75eo96KQy3UT20EkvmvBG0hXZuKAnb6Z9KlooAhmtkmtWt/uIV2jYB8vpisvWw0WmvFLc3Mn2g+SscUaFmLA8DOB0B6ntW1Ve8sre/g8m4QsgYMNrFSCOhBBBB+lAHJWvgLR7u3hu4b2+McoEyElBkMvH8Poaf/AMK30rZt+2X2NgT7ydB0/hrr4oo4IUhiQJHGoVVHQAcAU+tPbVO5l7Gn2OP/AOFdaZv3fbL3O4v95OuMf3aaPhvpQUL9svsBVX7ydAcj+Guyoo9tU7h7Gn2PN7rw3olrqMtqZtUM8brtXMX74vtGEzjP3hk9BzWhZ+AtGvbGC5gvL8xSxIyZKA4HIz8vWuputGsb2cz3EcjyYwp85xs5ByuD8pyq8jB4q3BBFa28dvAgSKJQiKOgAGAKPbVO4/Y0+xyf/CutL3bvtl7ncW+8nUjH92kHw30oKF+2X2Aqr95OinI/hrsaqahfrYQxuY3leWRYo40xlmPQZPA7n8KPbVO4vY0+xzEnw70xEZxc37su5gqtHliewyuKwX0Xw5FDBK1xqqwyW8UxOIztyrOinj7xCN0yOnIrto/Een3VtLn7SjKxhkRY2LLJnaUBXPzZ9D056VnSx+F7aGGKSOV1SNrdI8TSHavmKQRySVAkGTyBnFHtqncfsafYo2HgbSL62E8dzqCbZJEZHaPcrBirA4UjqD0NWP8AhXGlgY+2X3RR95P4Tkfw1pQa/oOnwvbQXDERNlgqPIxLBnLE4JOQrkn2NXP7f07GfOYKWKI5jba7AgEKcfNyccUe2qdxexp9jB/4Vzpf/P5ffxfxJ/F1/hqrqPgbSNN0+e9kn1KSOFVdljaPdhe/IA4613dV72zh1CyltLkM0Mq7XCuVJHcZBBo9tU7h7Gn2PPpfDeiQX1xamXVGlhcRpGDEGlaQgnbnGRyOTgDB5rp49CtNUmsNZS9uSyxRtCcIBt6rkbfetGbQ7C4cyTJLI+0KrtO5KYIPynPynKgkjGcc1ehhjt4I4IUCRRqERR0UAYApOpN7spU4LZGNrPhqHXYEhvby52pnHlhB1GD/AA1JpPh9NFgeG0vLjY7bjvCE5wB/d9q2KKXM7WvoVyq9+pl6jcS6bAs0k9zJGXCsY0j+XJABwcE8kcDJ9q5ie40yXV11SWfUTcRDzE2xxhfJXd+8x3Xhvf2rsbzTre/a3acSE28nmR7JGXDYxngjPU9aqHw3pJzm16tuP7xvf5ev3eT8v3eelJNrYGk9y2bSRnQyXUjqrBtu1Rkj6CirVFIYUUUUAFFFFABRRRQAUUUUAFFFFABUN1aQXsBhuIw6ZDYyQQQcggjkEHuKKKAKY8P6WqFVtQikLkI7LyDkNwfvcfe6+9RXHhjSrmYSvbsG3722yMN3DDB54HzsTjGSee9FFAEieHtJjeR0sowZMhsE4wQwIAzgD536f3jQ+gWDS27LGUWC4+0rGDlfMwQDg9PvE8Y5oooA06KKKACiiigAooooAKKKKACiiigD/9k=",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "occupancy_variables = ['md_occupancies', 'sg_occupancies', 't3_occupancies', 't5_occupancies']\n",
+    "percents = [99.99, 99.9, 99.9, 99.99]\n",
+    "compute_occupancies(branches, occupancy_variables, occ_percentiles=percents, plot=False)  # To print\n",
+    "compute_occupancies(branches, occupancy_variables, occ_percentiles=percents, plot=True)  # To plot"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/RecoTracker/LSTCore/standalone/bin/lst.cc b/RecoTracker/LSTCore/standalone/bin/lst.cc
index 369680bc4309e..17eb4a54628d6 100644
--- a/RecoTracker/LSTCore/standalone/bin/lst.cc
+++ b/RecoTracker/LSTCore/standalone/bin/lst.cc
@@ -53,6 +53,7 @@ int main(int argc, char **argv) {
       cxxopts::value<std::string>()->default_value("trackingNtuple/tree"))(
       "o,output", "Output file name", cxxopts::value<std::string>())(
       "N,nmatch", "N match for MTV-like matching", cxxopts::value<int>()->default_value("9"))(
+      "p,ptCut", "Min pT cut In GeV", cxxopts::value<float>()->default_value("0.8"))(
       "n,nevents", "N events to loop over", cxxopts::value<int>()->default_value("-1"))(
       "x,event_index", "specific event index to process", cxxopts::value<int>()->default_value("-1"))(
       "g,pdg_id", "The simhit pdgId match option", cxxopts::value<int>()->default_value("0"))(
@@ -145,6 +146,10 @@ int main(int argc, char **argv) {
     }
   }
 
+  //_______________________________________________________________________________
+  // --ptCut
+  ana.ptCut = result["ptCut"].as<float>();
+
   //_______________________________________________________________________________
   // --nmatch
   ana.nmatch_threshold = result["nmatch"].as<int>();
@@ -308,7 +313,9 @@ void run_lst() {
   // Load various maps used in the lst reconstruction
   TStopwatch full_timer;
   full_timer.Start();
-  auto hostESData = lst::loadAndFillESHost();
+  // Determine which maps to use based on given pt cut for standalone.
+  std::string ptCutString = (ana.ptCut >= 0.8) ? "0.8" : "0.6";
+  auto hostESData = lst::loadAndFillESHost(ptCutString);
   auto deviceESData =
       cms::alpakatools::CopyToDevice<lst::LSTESData<alpaka_common::DevHost>>::copyAsync(queues[0], *hostESData.get());
   float timeForMapLoading = full_timer.RealTime() * 1000;
@@ -388,7 +395,7 @@ void run_lst() {
   full_timer.Start();
   std::vector<LSTEvent *> events;
   for (int s = 0; s < ana.streams; s++) {
-    LSTEvent *event = new LSTEvent(ana.verbose >= 2, queues[s], &deviceESData);
+    LSTEvent *event = new LSTEvent(ana.verbose >= 2, ana.ptCut, queues[s], &deviceESData);
     events.push_back(event);
   }
   float timeForEventCreation = full_timer.RealTime() * 1000;
diff --git a/RecoTracker/LSTCore/standalone/bin/lst_make_tracklooper b/RecoTracker/LSTCore/standalone/bin/lst_make_tracklooper
index 7686b3df42bf5..0c3c9be329e91 100755
--- a/RecoTracker/LSTCore/standalone/bin/lst_make_tracklooper
+++ b/RecoTracker/LSTCore/standalone/bin/lst_make_tracklooper
@@ -28,7 +28,6 @@ usage()
   echo "  -G    GPU (CUDA) backend        (Compile for CUDA)"
   echo "  -R    ROCm backend              (Compile for ROCm)"
   echo "  -A    All backends              (Compile for all backends, including ROCm)"
-  echo "  -P    PT Cut Value              (In GeV, Default is 0.8, Works only for standalone version of code)"
   echo "  -w    Warning mode              (Print extra warning outputs)"
   echo
   exit
@@ -47,7 +46,6 @@ while getopts ":cxgsmdp3NCGRA2ehwP:" OPTION; do
     R) ROCMBACKEND=true;;
     A) ALLBACKENDS=true;;
     w) PRINTWARNINGS=true;;
-    P) PTCUTVALUE=$OPTARG;;
     h) usage;;
     :) usage;;
   esac
@@ -64,7 +62,6 @@ if [ -z ${CUDABACKEND} ]; then CUDABACKEND=false; fi
 if [ -z ${ROCMBACKEND} ]; then ROCMBACKEND=false; fi
 if [ -z ${ALLBACKENDS} ]; then ALLBACKENDS=false; fi
 if [ -z ${PRINTWARNINGS} ]; then PRINTWARNINGS=false; fi
-if [ -z ${PTCUTVALUE} ]; then PTCUTVALUE=0.8; fi
 
 # Default to only CPU and CUDA backends
 if [ "${CPUBACKEND}" == false ] && [ "${CUDABACKEND}" == false ] && [ "${ROCMBACKEND}" == false ]; then
@@ -101,7 +98,6 @@ echo "  CPUBACKEND        : ${CPUBACKEND}"                    | tee -a ${LOG}
 echo "  CUDABACKEND       : ${CUDABACKEND}"                   | tee -a ${LOG}
 echo "  ROCMBACKEND       : ${ROCMBACKEND}"                   | tee -a ${LOG}
 echo "  PRINTWARNINGS     : ${PRINTWARNINGS}"                 | tee -a ${LOG}
-echo "  PTCUTVALUE        : ${PTCUTVALUE} GeV"                | tee -a ${LOG}
 echo ""                                                       | tee -a ${LOG}
 echo "  (cf. Run > sh $(basename $0) -h to see all options)"  | tee -a ${LOG}
 echo ""                                                       | tee -a ${LOG}
@@ -159,8 +155,6 @@ if $PRINTWARNINGS; then
     PRINTWARNINGSOPT="LSTWARNINGSFLAG=-DWARNINGS"
 fi
 
-PTCUTOPT="PTCUTFLAG=-DPT_CUT=${PTCUTVALUE}"
-
 if [ -z "${MAXMAKETHREADS}" ]; then
     MAXMAKETHREADS=32
 fi
@@ -177,9 +171,9 @@ echo "--------------------------------------------------------------------------
 echo "---------------------------------------------------------------------------------------------" >> ${LOG} 2>&1 
 echo "---------------------------------------------------------------------------------------------" >> ${LOG} 2>&1 
 if $SHOWLOG; then
-    (cd LST && make clean && make ${BACKENDOPT} ${PRINTWARNINGSOPT} ${PTCUTOPT} -j ${MAXMAKETHREADS} ${MAKETARGET} && cd -) 2>&1 | tee -a ${LOG}
+    (cd LST && make clean && make ${BACKENDOPT} ${PRINTWARNINGSOPT} -j ${MAXMAKETHREADS} ${MAKETARGET} && cd -) 2>&1 | tee -a ${LOG}
 else
-    (cd LST && make clean && make ${BACKENDOPT} ${PRINTWARNINGSOPT} ${PTCUTOPT} -j ${MAXMAKETHREADS} ${MAKETARGET} && cd -) >> ${LOG} 2>&1
+    (cd LST && make clean && make ${BACKENDOPT} ${PRINTWARNINGSOPT} -j ${MAXMAKETHREADS} ${MAKETARGET} && cd -) >> ${LOG} 2>&1
 fi
 
 if ([[ "$BACKENDOPT" == *"all"* ]] || [[ "$BACKENDOPT" == *"cpu"* ]]) && [ ! -f LST/liblst_cpu.so ]; then
@@ -214,9 +208,9 @@ echo "--------------------------------------------------------------------------
 echo "---------------------------------------------------------------------------------------------" >> ${LOG} 2>&1 
 echo "---------------------------------------------------------------------------------------------" >> ${LOG} 2>&1 
 if $SHOWLOG; then
-    make EXES="${EXES}" ${TRACKLOOPERTARGET} ${PTCUTOPT} -j ${MAXMAKETHREADS} 2>&1 | tee -a ${LOG}
+    make EXES="${EXES}" ${TRACKLOOPERTARGET} -j ${MAXMAKETHREADS} 2>&1 | tee -a ${LOG}
 else
-    make EXES="${EXES}" ${TRACKLOOPERTARGET} ${PTCUTOPT} -j ${MAXMAKETHREADS} >> ${LOG} 2>&1
+    make EXES="${EXES}" ${TRACKLOOPERTARGET} -j ${MAXMAKETHREADS} >> ${LOG} 2>&1
 fi
 
 if ([[ "$BACKENDOPT" == *"all"* ]] || [[ "$BACKENDOPT" == *"cpu"* ]]) && [ ! -f bin/lst_cpu ]; then
diff --git a/RecoTracker/LSTCore/standalone/code/core/AnalysisConfig.h b/RecoTracker/LSTCore/standalone/code/core/AnalysisConfig.h
index 8608bc95ed2fa..6d0da61bf2395 100644
--- a/RecoTracker/LSTCore/standalone/code/core/AnalysisConfig.h
+++ b/RecoTracker/LSTCore/standalone/code/core/AnalysisConfig.h
@@ -46,6 +46,9 @@ class AnalysisConfig {
   // pt binning options
   int ptbound_mode;
 
+  // pt cut
+  float ptCut;
+
   // pdg id of the particles to compute efficincies on
   int pdg_id;
 
diff --git a/RecoTracker/LSTCore/standalone/code/core/trkCore.cc b/RecoTracker/LSTCore/standalone/code/core/trkCore.cc
index ffb2e7de205ac..e480aa3608a4b 100644
--- a/RecoTracker/LSTCore/standalone/code/core/trkCore.cc
+++ b/RecoTracker/LSTCore/standalone/code/core/trkCore.cc
@@ -291,7 +291,8 @@ std::vector<int> matchedSimTrkIdxs(std::vector<int> hitidxs, std::vector<int> hi
 //___________________________________________________________________________________________________________________________________________________________________________________________
 std::vector<int> matchedSimTrkIdxs(std::vector<unsigned int> hitidxs,
                                    std::vector<unsigned int> hittypes,
-                                   bool verbose) {
+                                   bool verbose,
+                                   float *pmatched) {
   if (hitidxs.size() != hittypes.size()) {
     std::cout << "Error: matched_sim_trk_idxs()   hitidxs and hittypes have different lengths" << std::endl;
     std::cout << "hitidxs.size(): " << hitidxs.size() << std::endl;
@@ -425,6 +426,7 @@ std::vector<int> matchedSimTrkIdxs(std::vector<unsigned int> hitidxs,
   }
   int maxHitMatchCount = 0;  // ultimate maximum of the number of matched hits
   std::vector<int> matched_sim_trk_idxs;
+  float max_percent_matched = 0.0f;
   for (auto &trkidx_perm : allperms) {
     std::vector<int> counts;
     for (auto &unique_idx : unique_idxs) {
@@ -436,10 +438,18 @@ std::vector<int> matchedSimTrkIdxs(std::vector<unsigned int> hitidxs,
     int trkidx = unique_idxs[rawidx];
     if (trkidx < 0)
       continue;
-    if (counts[rawidx] > (((float)nhits_input) * 0.75))
+    float percent_matched = static_cast<float>(counts[rawidx]) / nhits_input;
+    if (percent_matched > 0.75f)
       matched_sim_trk_idxs.push_back(trkidx);
     maxHitMatchCount = std::max(maxHitMatchCount, *std::max_element(counts.begin(), counts.end()));
+    max_percent_matched = std::max(max_percent_matched, percent_matched);
   }
+
+  // If pmatched is provided, set its value
+  if (pmatched != nullptr) {
+    *pmatched = max_percent_matched;
+  }
+
   std::set<int> s;
   unsigned size = matched_sim_trk_idxs.size();
   for (unsigned i = 0; i < size; ++i)
@@ -701,7 +711,7 @@ void addInputsToLineSegmentTrackingPreLoad(std::vector<std::vector<float>> &out_
     float eta = p3LH.Eta();
     float ptErr = trk.see_ptErr()[iSeed];
 
-    if ((ptIn > PT_CUT - 2 * ptErr)) {
+    if ((ptIn > ana.ptCut - 2 * ptErr)) {
       TVector3 r3LH(trk.see_stateTrajGlbX()[iSeed], trk.see_stateTrajGlbY()[iSeed], trk.see_stateTrajGlbZ()[iSeed]);
       TVector3 p3PCA(trk.see_px()[iSeed], trk.see_py()[iSeed], trk.see_pz()[iSeed]);
       TVector3 r3PCA(calculateR3FromPCA(p3PCA, trk.see_dxy()[iSeed], trk.see_dz()[iSeed]));
@@ -722,7 +732,7 @@ void addInputsToLineSegmentTrackingPreLoad(std::vector<std::vector<float>> &out_
       PixelType pixtype = PixelType::kInvalid;
       if (ptIn >= 2.0) {
         pixtype = PixelType::kHighPt;
-      } else if (ptIn >= (PT_CUT - 2 * ptErr) and ptIn < 2.0) {
+      } else if (ptIn >= (ana.ptCut - 2 * ptErr) and ptIn < 2.0) {
         if (pixelSegmentDeltaPhiChange >= 0) {
           pixtype = PixelType::kLowPtPosCurv;
         } else {
diff --git a/RecoTracker/LSTCore/standalone/code/core/trkCore.h b/RecoTracker/LSTCore/standalone/code/core/trkCore.h
index e1ff0a00bd002..912143b052d9f 100644
--- a/RecoTracker/LSTCore/standalone/code/core/trkCore.h
+++ b/RecoTracker/LSTCore/standalone/code/core/trkCore.h
@@ -34,7 +34,8 @@ float runpT3(LSTEvent *event);
 
 std::vector<int> matchedSimTrkIdxs(std::vector<unsigned int> hitidxs,
                                    std::vector<unsigned int> hittypes,
-                                   bool verbose = false);
+                                   bool verbose = false,
+                                   float *pmatched = nullptr);
 std::vector<int> matchedSimTrkIdxs(std::vector<int> hitidxs, std::vector<int> hittypes, bool verbose = false);
 int getDenomSimTrkType(int isimtrk);
 int getDenomSimTrkType(std::vector<int> simidxs);
diff --git a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc
index 1da3099947c79..deed88f833a00 100644
--- a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc
+++ b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc
@@ -1,3 +1,7 @@
+// to use computeRadiusFromThreeAnchorHits
+#include "LSTEvent.h"
+#include "Triplet.h"
+
 #include "write_lst_ntuple.h"
 
 using namespace ALPAKA_ACCELERATOR_NAMESPACE::lst;
@@ -72,6 +76,8 @@ void createOptionalOutputBranches() {
   ana.tx->createBranch<std::vector<float>>("pT5_eta");
   ana.tx->createBranch<std::vector<float>>("pT5_phi");
   ana.tx->createBranch<std::vector<int>>("pT5_isFake");
+  ana.tx->createBranch<std::vector<float>>("t5_sim_vxy");
+  ana.tx->createBranch<std::vector<float>>("t5_sim_vz");
   ana.tx->createBranch<std::vector<int>>("pT5_isDuplicate");
   ana.tx->createBranch<std::vector<int>>("pT5_score");
   ana.tx->createBranch<std::vector<int>>("pT5_layer_binary");
@@ -118,6 +124,7 @@ void createOptionalOutputBranches() {
   ana.tx->createBranch<std::vector<int>>("t5_isDuplicate");
   ana.tx->createBranch<std::vector<int>>("t5_foundDuplicate");
   ana.tx->createBranch<std::vector<float>>("t5_pt");
+  ana.tx->createBranch<std::vector<float>>("t5_pMatched");
   ana.tx->createBranch<std::vector<float>>("t5_eta");
   ana.tx->createBranch<std::vector<float>>("t5_phi");
   ana.tx->createBranch<std::vector<float>>("t5_score_rphisum");
@@ -126,17 +133,65 @@ void createOptionalOutputBranches() {
   ana.tx->createBranch<std::vector<int>>("t5_moduleType_binary");
   ana.tx->createBranch<std::vector<int>>("t5_layer_binary");
   ana.tx->createBranch<std::vector<float>>("t5_matched_pt");
-  ana.tx->createBranch<std::vector<int>>("t5_partOfTC");
   ana.tx->createBranch<std::vector<float>>("t5_innerRadius");
   ana.tx->createBranch<std::vector<float>>("t5_outerRadius");
   ana.tx->createBranch<std::vector<float>>("t5_bridgeRadius");
   ana.tx->createBranch<std::vector<float>>("t5_chiSquared");
   ana.tx->createBranch<std::vector<float>>("t5_rzChiSquared");
   ana.tx->createBranch<std::vector<float>>("t5_nonAnchorChiSquared");
+  ana.tx->createBranch<std::vector<float>>("t5_dBeta1");
+  ana.tx->createBranch<std::vector<float>>("t5_dBeta2");
+
+  // Occupancy branches
+  ana.tx->createBranch<std::vector<int>>("module_layers");
+  ana.tx->createBranch<std::vector<int>>("module_subdets");
+  ana.tx->createBranch<std::vector<int>>("module_rings");
+  ana.tx->createBranch<std::vector<int>>("module_rods");
+  ana.tx->createBranch<std::vector<int>>("module_modules");
+  ana.tx->createBranch<std::vector<bool>>("module_isTilted");
+  ana.tx->createBranch<std::vector<float>>("module_eta");
+  ana.tx->createBranch<std::vector<float>>("module_r");
+  ana.tx->createBranch<std::vector<int>>("md_occupancies");
+  ana.tx->createBranch<std::vector<int>>("sg_occupancies");
+  ana.tx->createBranch<std::vector<int>>("t3_occupancies");
+  ana.tx->createBranch<int>("tc_occupancies");
+  ana.tx->createBranch<std::vector<int>>("t5_occupancies");
+  ana.tx->createBranch<int>("pT3_occupancies");
+  ana.tx->createBranch<int>("pT5_occupancies");
+
+  // T5 DNN branches
+  createT5DNNBranches();
 
 #endif
 }
 
+//________________________________________________________________________________________________________________________________
+void createT5DNNBranches() {
+  // Common branches
+  ana.tx->createBranch<std::vector<int>>("t5_t3_idx0");
+  ana.tx->createBranch<std::vector<int>>("t5_t3_idx1");
+  ana.tx->createBranch<std::vector<int>>("t5_tc_idx");
+  ana.tx->createBranch<std::vector<int>>("t5_partOfTC");
+  ana.tx->createBranch<std::vector<float>>("t5_t3_pt");
+  ana.tx->createBranch<std::vector<float>>("t5_t3_eta");
+  ana.tx->createBranch<std::vector<float>>("t5_t3_phi");
+
+  // Hit-specific branches
+  std::vector<std::string> hitIndices = {"0", "1", "2", "3", "4", "5"};
+  std::vector<std::string> hitProperties = {"r", "x", "y", "z", "eta", "phi", "detId", "layer", "moduleType"};
+
+  for (const auto& idx : hitIndices) {
+    for (const auto& prop : hitProperties) {
+      std::string branchName = "t5_t3_" + idx + "_" + prop;
+      if (prop == "detId" || prop == "layer" || prop == "moduleType") {
+        ana.tx->createBranch<std::vector<int>>(branchName);
+      } else {
+        ana.tx->createBranch<std::vector<float>>(branchName);
+      }
+    }
+  }
+}
+
 //________________________________________________________________________________________________________________________________
 void createGnnNtupleBranches() {
   // Mini Doublets
@@ -284,10 +339,74 @@ void setOptionalOutputBranches(LSTEvent* event) {
   setPixelQuintupletOutputBranches(event);
   setQuintupletOutputBranches(event);
   setPixelTripletOutputBranches(event);
+  setOccupancyBranches(event);
+  setT5DNNBranches(event);
 
 #endif
 }
 
+//________________________________________________________________________________________________________________________________
+void setOccupancyBranches(LSTEvent* event) {
+  auto modules = event->getModules<ModulesSoA>();
+  auto miniDoublets = event->getMiniDoublets<MiniDoubletsOccupancySoA>();
+  auto segments = event->getSegments<SegmentsOccupancySoA>();
+  auto triplets = event->getTriplets<TripletsOccupancySoA>();
+  auto quintuplets = event->getQuintuplets<QuintupletsOccupancySoA>();
+  auto pixelQuintuplets = event->getPixelQuintuplets();
+  auto pixelTriplets = event->getPixelTriplets();
+  auto trackCandidates = event->getTrackCandidates();
+
+  std::vector<int> moduleLayer;
+  std::vector<int> moduleSubdet;
+  std::vector<int> moduleRing;
+  std::vector<int> moduleRod;
+  std::vector<int> moduleModule;
+  std::vector<float> moduleEta;
+  std::vector<float> moduleR;
+  std::vector<bool> moduleIsTilted;
+  std::vector<int> trackCandidateOccupancy;
+  std::vector<int> tripletOccupancy;
+  std::vector<int> segmentOccupancy;
+  std::vector<int> mdOccupancy;
+  std::vector<int> quintupletOccupancy;
+
+  for (unsigned int lowerIdx = 0; lowerIdx <= modules.nLowerModules(); lowerIdx++) {
+    //layer = 0, subdet = 0 => pixel module
+    moduleLayer.push_back(modules.layers()[lowerIdx]);
+    moduleSubdet.push_back(modules.subdets()[lowerIdx]);
+    moduleRing.push_back(modules.rings()[lowerIdx]);
+    moduleRod.push_back(modules.rods()[lowerIdx]);
+    moduleEta.push_back(modules.eta()[lowerIdx]);
+    moduleR.push_back(modules.r()[lowerIdx]);
+    bool isTilted = (modules.subdets()[lowerIdx] == 5 and modules.sides()[lowerIdx] != 3);
+    moduleIsTilted.push_back(isTilted);
+    moduleModule.push_back(modules.modules()[lowerIdx]);
+    segmentOccupancy.push_back(segments.totOccupancySegments()[lowerIdx]);
+    mdOccupancy.push_back(miniDoublets.totOccupancyMDs()[lowerIdx]);
+
+    if (lowerIdx < modules.nLowerModules()) {
+      quintupletOccupancy.push_back(quintuplets.totOccupancyQuintuplets()[lowerIdx]);
+      tripletOccupancy.push_back(triplets.totOccupancyTriplets()[lowerIdx]);
+    }
+  }
+
+  ana.tx->setBranch<std::vector<int>>("module_layers", moduleLayer);
+  ana.tx->setBranch<std::vector<int>>("module_subdets", moduleSubdet);
+  ana.tx->setBranch<std::vector<int>>("module_rings", moduleRing);
+  ana.tx->setBranch<std::vector<int>>("module_rods", moduleRod);
+  ana.tx->setBranch<std::vector<int>>("module_modules", moduleModule);
+  ana.tx->setBranch<std::vector<bool>>("module_isTilted", moduleIsTilted);
+  ana.tx->setBranch<std::vector<float>>("module_eta", moduleEta);
+  ana.tx->setBranch<std::vector<float>>("module_r", moduleR);
+  ana.tx->setBranch<std::vector<int>>("md_occupancies", mdOccupancy);
+  ana.tx->setBranch<std::vector<int>>("sg_occupancies", segmentOccupancy);
+  ana.tx->setBranch<std::vector<int>>("t3_occupancies", tripletOccupancy);
+  ana.tx->setBranch<int>("tc_occupancies", trackCandidates.nTrackCandidates());
+  ana.tx->setBranch<int>("pT3_occupancies", pixelTriplets.totOccupancyPixelTriplets());
+  ana.tx->setBranch<std::vector<int>>("t5_occupancies", quintupletOccupancy);
+  ana.tx->setBranch<int>("pT5_occupancies", pixelQuintuplets.totOccupancyPixelQuintuplets());
+}
+
 //________________________________________________________________________________________________________________________________
 void setPixelQuintupletOutputBranches(LSTEvent* event) {
   // ============ pT5 =============
@@ -325,6 +444,7 @@ void setPixelQuintupletOutputBranches(LSTEvent* event) {
     ana.tx->pushbackToBranch<float>("pT5_phi", phi);
     ana.tx->pushbackToBranch<int>("pT5_layer_binary", layer_binary);
     ana.tx->pushbackToBranch<int>("pT5_moduleType_binary", moduleType_binary);
+    ana.tx->pushbackToBranch<float>("pT5_rzChiSquared", pixelQuintuplets.rzChiSquared()[pT5]);
 
     pT5_matched_simIdx.push_back(simidx);
 
@@ -393,10 +513,12 @@ void setQuintupletOutputBranches(LSTEvent* event) {
         moduleType_binary |= (modules.moduleType()[module_idx[i]] << i);
       }
 
-      std::vector<int> simidx = matchedSimTrkIdxs(hit_idx, hit_type);
+      float percent_matched;
+      std::vector<int> simidx = matchedSimTrkIdxs(hit_idx, hit_type, false, &percent_matched);
 
       ana.tx->pushbackToBranch<int>("t5_isFake", static_cast<int>(simidx.size() == 0));
       ana.tx->pushbackToBranch<float>("t5_pt", pt);
+      ana.tx->pushbackToBranch<float>("t5_pMatched", percent_matched);
       ana.tx->pushbackToBranch<float>("t5_eta", eta);
       ana.tx->pushbackToBranch<float>("t5_phi", phi);
       ana.tx->pushbackToBranch<float>("t5_innerRadius", __H2F(quintuplets.innerRadius()[quintupletIndex]));
@@ -404,6 +526,9 @@ void setQuintupletOutputBranches(LSTEvent* event) {
       ana.tx->pushbackToBranch<float>("t5_outerRadius", __H2F(quintuplets.outerRadius()[quintupletIndex]));
       ana.tx->pushbackToBranch<float>("t5_chiSquared", quintuplets.chiSquared()[quintupletIndex]);
       ana.tx->pushbackToBranch<float>("t5_rzChiSquared", quintuplets.rzChiSquared()[quintupletIndex]);
+      ana.tx->pushbackToBranch<float>("t5_nonAnchorChiSquared", quintuplets.nonAnchorChiSquared()[quintupletIndex]);
+      ana.tx->pushbackToBranch<float>("t5_dBeta1", quintuplets.dBeta1()[quintupletIndex]);
+      ana.tx->pushbackToBranch<float>("t5_dBeta2", quintuplets.dBeta2()[quintupletIndex]);
       ana.tx->pushbackToBranch<int>("t5_layer_binary", layer_binary);
       ana.tx->pushbackToBranch<int>("t5_moduleType_binary", moduleType_binary);
 
@@ -414,6 +539,21 @@ void setQuintupletOutputBranches(LSTEvent* event) {
           sim_t5_matched.at(simtrk) += 1;
         }
       }
+
+      // Avoid fakes when calculating the vertex distance, set default to 0.0.
+      if (simidx.size() == 0) {
+        ana.tx->pushbackToBranch<float>("t5_sim_vxy", 0.0);
+        ana.tx->pushbackToBranch<float>("t5_sim_vz", 0.0);
+        continue;
+      }
+
+      int vtxidx = trk.sim_parentVtxIdx()[simidx[0]];
+      float vtx_x = trk.simvtx_x()[vtxidx];
+      float vtx_y = trk.simvtx_y()[vtxidx];
+      float vtx_z = trk.simvtx_z()[vtxidx];
+
+      ana.tx->pushbackToBranch<float>("t5_sim_vxy", sqrt(vtx_x * vtx_x + vtx_y * vtx_y));
+      ana.tx->pushbackToBranch<float>("t5_sim_vz", vtx_z);
     }
   }
 
@@ -498,6 +638,109 @@ void setPixelTripletOutputBranches(LSTEvent* event) {
   ana.tx->setBranch<std::vector<int>>("pT3_isDuplicate", pT3_isDuplicate);
 }
 
+//________________________________________________________________________________________________________________________________
+void fillT5DNNBranches(LSTEvent* event, unsigned int iT3) {
+  auto hits = event->getHits<HitsSoA>();
+  auto modules = event->getModules<ModulesSoA>();
+
+  std::vector<unsigned int> hitIdx = getHitsFromT3(event, iT3);
+  std::vector<lst_math::Hit> hitObjects(hitIdx.size());
+
+  for (int i = 0; i < hitIdx.size(); ++i) {
+    unsigned int hit = hitIdx[i];
+    float x = hits.xs()[hit];
+    float y = hits.ys()[hit];
+    float z = hits.zs()[hit];
+    hitObjects[i] = lst_math::Hit(x, y, z);
+
+    std::string idx = std::to_string(i);
+    ana.tx->pushbackToBranch<float>("t5_t3_" + idx + "_r", sqrt(x * x + y * y));
+    ana.tx->pushbackToBranch<float>("t5_t3_" + idx + "_x", x);
+    ana.tx->pushbackToBranch<float>("t5_t3_" + idx + "_y", y);
+    ana.tx->pushbackToBranch<float>("t5_t3_" + idx + "_z", z);
+    ana.tx->pushbackToBranch<float>("t5_t3_" + idx + "_eta", hitObjects[i].eta());
+    ana.tx->pushbackToBranch<float>("t5_t3_" + idx + "_phi", hitObjects[i].phi());
+
+    int subdet = trk.ph2_subdet()[hits.idxs()[hit]];
+    int is_endcap = subdet == 4;
+    int layer = trk.ph2_layer()[hits.idxs()[hit]] + 6 * is_endcap;
+    int detId = trk.ph2_detId()[hits.idxs()[hit]];
+    unsigned int module = hits.moduleIndices()[hit];
+
+    ana.tx->pushbackToBranch<int>("t5_t3_" + idx + "_detId", detId);
+    ana.tx->pushbackToBranch<int>("t5_t3_" + idx + "_layer", layer);
+    ana.tx->pushbackToBranch<int>("t5_t3_" + idx + "_moduleType", modules.moduleType()[module]);
+  }
+
+  float g, f;
+  auto const& devHost = cms::alpakatools::host();
+  float radius = computeRadiusFromThreeAnchorHits(devHost,
+                                                  hitObjects[0].x(),
+                                                  hitObjects[0].y(),
+                                                  hitObjects[1].x(),
+                                                  hitObjects[1].y(),
+                                                  hitObjects[2].x(),
+                                                  hitObjects[2].y(),
+                                                  g,
+                                                  f);
+  ana.tx->pushbackToBranch<float>("t5_t3_pt", k2Rinv1GeVf * 2 * radius);
+
+  // Angles
+  ana.tx->pushbackToBranch<float>("t5_t3_eta", hitObjects[2].eta());
+  ana.tx->pushbackToBranch<float>("t5_t3_phi", hitObjects[0].phi());
+}
+
+//________________________________________________________________________________________________________________________________
+void setT5DNNBranches(LSTEvent* event) {
+  auto triplets = event->getTriplets<TripletsOccupancySoA>();
+  auto modules = event->getModules<ModulesSoA>();
+  auto ranges = event->getRanges();
+  auto const quintuplets = event->getQuintuplets<QuintupletsOccupancySoA>();
+  auto trackCandidates = event->getTrackCandidates();
+
+  std::unordered_set<unsigned int> allT3s;
+  std::unordered_map<unsigned int, unsigned int> t3_index_map;
+
+  for (unsigned int idx = 0; idx < modules.nLowerModules(); ++idx) {
+    for (unsigned int jdx = 0; jdx < triplets.nTriplets()[idx]; ++jdx) {
+      unsigned int t3Idx = ranges.tripletModuleIndices()[idx] + jdx;
+      if (allT3s.insert(t3Idx).second) {
+        t3_index_map[t3Idx] = allT3s.size() - 1;
+        fillT5DNNBranches(event, t3Idx);
+      }
+    }
+  }
+
+  std::unordered_map<unsigned int, unsigned int> t5_tc_index_map;
+  std::unordered_set<unsigned int> t5s_used_in_tc;
+
+  for (unsigned int idx = 0; idx < trackCandidates.nTrackCandidates(); idx++) {
+    if (trackCandidates.trackCandidateType()[idx] == LSTObjType::T5) {
+      unsigned int objIdx = trackCandidates.directObjectIndices()[idx];
+      t5s_used_in_tc.insert(objIdx);
+      t5_tc_index_map[objIdx] = idx;
+    }
+  }
+
+  for (unsigned int idx = 0; idx < modules.nLowerModules(); ++idx) {
+    for (unsigned int jdx = 0; jdx < quintuplets.nQuintuplets()[idx]; ++jdx) {
+      unsigned int t5Idx = ranges.quintupletModuleIndices()[idx] + jdx;
+      std::vector<unsigned int> t3sIdx = getT3sFromT5(event, t5Idx);
+
+      ana.tx->pushbackToBranch<int>("t5_t3_idx0", t3_index_map[t3sIdx[0]]);
+      ana.tx->pushbackToBranch<int>("t5_t3_idx1", t3_index_map[t3sIdx[1]]);
+
+      if (t5s_used_in_tc.find(t5Idx) != t5s_used_in_tc.end()) {
+        ana.tx->pushbackToBranch<int>("t5_partOfTC", 1);
+        ana.tx->pushbackToBranch<int>("t5_tc_idx", t5_tc_index_map[t5Idx]);
+      } else {
+        ana.tx->pushbackToBranch<int>("t5_partOfTC", 0);
+        ana.tx->pushbackToBranch<int>("t5_tc_idx", -999);
+      }
+    }
+  }
+}
+
 //________________________________________________________________________________________________________________________________
 void setGnnNtupleBranches(LSTEvent* event) {
   // Get relevant information
@@ -717,16 +960,16 @@ std::tuple<int, float, float, float, int, std::vector<int>> parseTrackCandidate(
   float pt, eta, phi;
   std::vector<unsigned int> hit_idx, hit_type;
   switch (type) {
-    case lst::LSTObjType::pT5:
+    case LSTObjType::pT5:
       std::tie(pt, eta, phi, hit_idx, hit_type) = parsepT5(event, idx);
       break;
-    case lst::LSTObjType::pT3:
+    case LSTObjType::pT3:
       std::tie(pt, eta, phi, hit_idx, hit_type) = parsepT3(event, idx);
       break;
-    case lst::LSTObjType::T5:
+    case LSTObjType::T5:
       std::tie(pt, eta, phi, hit_idx, hit_type) = parseT5(event, idx);
       break;
-    case lst::LSTObjType::pLS:
+    case LSTObjType::pLS:
       std::tie(pt, eta, phi, hit_idx, hit_type) = parsepLS(event, idx);
       break;
   }
diff --git a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.h b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.h
index 314096f72679e..5bfe439fadbb3 100644
--- a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.h
+++ b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.h
@@ -18,15 +18,19 @@ void createOutputBranches();
 void createRequiredOutputBranches();
 void createOptionalOutputBranches();
 void createGnnNtupleBranches();
+void createT5DNNBranches();
 
 void fillOutputBranches(LSTEvent* event);
 void setOutputBranches(LSTEvent* event);
 void setOptionalOutputBranches(LSTEvent* event);
+void setOccupancyBranches(LSTEvent* event);
 void setPixelQuintupletOutputBranches(LSTEvent* event);
 void setQuintupletOutputBranches(LSTEvent* event);
 void setPixelTripletOutputBranches(LSTEvent* event);
 void setGnnNtupleBranches(LSTEvent* event);
 void setGnnNtupleMiniDoublet(LSTEvent* event, unsigned int MD);
+void fillT5DNNBranches(LSTEvent* event, unsigned int T3);
+void setT5DNNBranches(LSTEvent* event);
 
 std::tuple<int, float, float, float, int, std::vector<int>> parseTrackCandidate(LSTEvent* event, unsigned int);
 std::tuple<float, float, float, std::vector<unsigned int>, std::vector<unsigned int>> parsepT5(LSTEvent* event,
diff --git a/RecoTracker/LSTCore/standalone/setup.sh b/RecoTracker/LSTCore/standalone/setup.sh
index 1a35fa8e69bdf..570a7ae9fee81 100644
--- a/RecoTracker/LSTCore/standalone/setup.sh
+++ b/RecoTracker/LSTCore/standalone/setup.sh
@@ -7,18 +7,20 @@ DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 source $DIR/code/rooutil/thisrooutil.sh
 
 ARCH=$(uname -m)
-if [[ $(hostname) == *lnx4555* ]]; then
-  export SCRAM_ARCH=el9_amd64_gcc12
-elif [[ $ARCH == "aarch64" || $ARCH == "arm64" ]]; then
-  export SCRAM_ARCH=el9_aarch64_gcc12
+if [ -z ${CMSSW_SEARCH_PATH+x} ]; then
+  if [ -z ${FORCED_CMSSW_VERSION+x} ]; then
+    export CMSSW_VERSION=CMSSW_14_2_0_pre4
+  else
+    export CMSSW_VERSION=$FORCED_CMSSW_VERSION
+  fi
+
+  source /cvmfs/cms.cern.ch/cmsset_default.sh
+  CMSSW_PATH=$(scram list -c CMSSW | grep -w $CMSSW_VERSION | awk '{print $3}')
+  cd $CMSSW_PATH
+  eval `scramv1 runtime -sh`
 else
-  export SCRAM_ARCH=el8_amd64_gcc12
+  cd $CMSSW_BASE/src
 fi
-export CMSSW_VERSION=CMSSW_14_2_0_pre3
-
-source /cvmfs/cms.cern.ch/cmsset_default.sh
-cd /cvmfs/cms.cern.ch/$SCRAM_ARCH/cms/cmssw/$CMSSW_VERSION/src
-eval `scramv1 runtime -sh`
 
 # Export paths to libraries we need
 export BOOST_ROOT=$(scram tool info boost | grep BOOST_BASE | cut -d'=' -f2)