  In the image data collected by astronomical surveys, stars and galaxies often
overlap. Deblending is the task of distinguishing and characterizing individual
light sources from survey images. We propose StarNet, a fully Bayesian method
to deblend sources in astronomical images of crowded star fields. StarNet
leverages recent advances in variational inference, including amortized
variational distributions and the wake-sleep algorithm. Wake-sleep, which
minimizes forward KL divergence, has significant benefits compared to
traditional variational inference, which minimizes a reverse KL divergence. In
our experiments with SDSS images of the M2 globular cluster, StarNet is
substantially more accurate than two competing methods: Probablistic Cataloging
(PCAT), a method that uses MCMC for inference, and a software pipeline employed
by SDSS for deblending (DAOPHOT). In addition, StarNet is as much as $100,000$
times faster than PCAT, exhibiting the scaling characteristics necessary to
perform fully Bayesian inference on modern astronomical surveys.
