2025 2 inbandsi

Goals

• Fit a spectrum from trusted surveys and/or NED within plot_field.py that can then be used later on within the pipeline.

Accomplishments

• Optionally plotting spectra for every source within the delay calibrator table and saving fitting parameters within delay_calibrators.csv.
• Currently it will save parameters from trusted surveys, and if that's unavailable it will save parameters from NED, stating which one, the $$\chi^2$$ and number of photometry points.

Early Example Plot:

For The source: --RA=193.747 --DEC=44.160

From Trusted Surveys:

From NED:

Delay Calibrator table (First and last 4 columns):

Errors

With errors included, some example plots appear as:

Example of a bad fit with poor $$\chi^2$$:

Final delay calibrator output:

>>> t.pprint()
Observation  RA_LBCS    DEC_LBCS    Date      Time      Goodness   ...       alpha_1              alpha_2             chi_sqr       phot_points Catalogue
----------- ---------- --------- ---------- -------- ------------- ... -------------------- -------------------- ------------------ ----------- ---------
    L332824 194.012583 54.305444 2015-03-19 00:08:05 PPP-PPPPP---- ...   0.6172451396889361  -1.5969233906620508 275.14121552830585           6   Trusted
    L332816 193.185125 54.380667 2015-03-19 00:08:05 PPP-PPSPP---- ...  -0.5964986436317669 -0.12418303465658431  5.673983939162344           7   Trusted
    L392189  194.56625 54.363667 2015-07-30 14:51:00 PPPPPPPPS---- ...   1.0724250772296493  -0.7642522548252477 11.228899260396702           6   Trusted
    L332808  194.68425 54.725028 2015-03-19 00:08:05 PSS-PXXXS---- ...  -0.5616858543168316  -0.3791695430515891 18.738270552590308           6   Trusted
    L332840  194.16075 53.573306 2015-03-19 00:08:05 PPP-PPPSX---- ... -0.30805241032606556  -0.1929866211643725  13.59106744123625           6   Trusted
    L332818 194.836667 54.538056 2015-03-19 00:08:05 SXX-XXXXX---- ...   0.3299003994350143  -0.8293662809084956    88.010605065373           5   Trusted

Implementation

• It is executable with the option --fit_spec in plot_field.py. It runs as:

1. Fitting from trusted catalogues
2. Check if $$\chi^2 > 20$$ and if it is, try to fit from NED. Also if Trusted cannot be fitted, for from NED.
3. Add results in to delay_calibrators.csv.

• If Ned has been successful, use NED (since if NED has been used there is an issue with trusted catalogues).
• Otherwise add results from Trusted Catalogue fit.

To Dos:

• Include errors from all surveys trusted surveys.
• Look into some possible issues from attempting to use NED