PyGFit: A Tool for Extracting PSF Matched Photometry
  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.
