PyGFit: A Tool for Extracting PSF Matched Photometry

Abstract

We present PyGFit, a program designed to measure PSF-matched photometry from images with disparate pixel scales and PSF sizes. While PyGFit has a number of uses, its primary purpose is to extract robust spectral energy distributions (SEDs) from crowded images. It does this by fitting blended sources in crowded, low resolution images with models generated from a higher resolution image. This approach minimizes the impact of crowding and also yields consistently measured fluxes in different filters, minimizing systematic uncertainty in the final SEDs. We present an example of applying PyGFit to real data and perform simulations to test its fidelity. The uncertainty in the best-fit flux rises sharply as a function of nearest-neighbor distance for objects with a neighbor within 60% of the PSF size. Similarly, the uncertainty increases quickly for objects blended with a neighbor more than four times brighter. For all other objects the fidelity of PyGFit's results depends only on flux, and the uncertainty is primarily limited by sky noise.