Photometric Completeness Modelled with Neural Networks

In almost any study involving optical/near-infrared photometry, understanding the completeness of detection and recovery is an essential part of the work. The recovery fraction is, in general, a function of several variables including magnitude, color, background sky noise, and crowding. We explore how completeness can be modeled, with the use of artificial-star tests, in a way that includes all of these parameters simultaneously within a …