  In images collected by astronomical surveys, stars and galaxies often overlap
visually. Deblending is the task of distinguishing and characterizing
individual light sources in survey images. We propose StarNet, a Bayesian
method to deblend sources in astronomical images of crowded star fields.
StarNet leverages recent advances in variational inference, including amortized
variational distributions and an optimization objective targeting an
expectation of the forward KL divergence. In our experiments with SDSS images
of the M2 globular cluster, StarNet is substantially more accurate than two
competing methods: Probabilistic Cataloging (PCAT), a method that uses MCMC for
inference, and DAOPHOT, a software pipeline employed by SDSS for deblending. In
addition, the amortized approach to inference gives StarNet the scaling
characteristics necessary to perform Bayesian inference on modern astronomical
surveys.
