If you use SeeKAT for a publication, please cite my paper: https://ui.adsabs.harvard.edu/abs/2023RASTI…2..114B

NB: A browser-based interactive app version of SeeKAT is now available here: https://github.com/BezuidenhoutMC/SeeKAT-app/

• Reads in list of detections (RA, Dec, S/N) and the beam PSF. PSFs can be modelled using MOSAIC (https://github.com/wchenastro/Mosaic).

• Computes a covariance matrix of the S/N value ratios, assuming 1-sigma Gaussian errors on each measurement.

• Models the aggregate beam response by arranging beam PSFs appropriately relative to each other.

• Calculates a likelihood distribution of obtaining the observed S/N in each beam according to the modelled response.

• Plots the likelihood function over RA and Dec with 1-sigma uncertainty, overlaid on the beam coordinates and sizes.

Usage: python SeeKAT.py -f {coordinates file} -p {.fits file} –r {PSF resolution} –o {fractional overlap}

OR

python SeeKAT.py -f {coordinates file} -p {.fits file} –c {.json file} –r {PSF resolution}

Customisation options:

–n Computes the likelihood map using only the n brightest pairs of beams.

–clip All values of the CB PSF below this value are set to zero. Helps negate low-level sidelobes.

–s In the format RA(hms),Dec(dms) adds a marker for known coordinates to plot.

–scalebar Sets the length of the scalebar on the plot in arcseconds. Set to 0 to omit it altogether.

–zoom Automatically zooms in on the TABs.

–ticks Sets the spacing between axis ticks in number of pixels.

–nsig Draws uncertainty contours up to this number of standard deviations.

–fits Writes likelihood to fits file.