  We present a new probabilistic method for detecting, deblending, and
cataloging astronomical sources called the Bayesian Light Source Separator
(BLISS). BLISS is based on deep generative models, which embed neural networks
within a Bayesian model. For posterior inference, BLISS uses a new form of
variational inference known as Forward Amortized Variational Inference. The
BLISS inference routine is fast, requiring a single forward pass of the encoder
networks on a GPU once the encoder networks are trained. BLISS can perform
fully Bayesian inference on megapixel images in seconds, and produces highly
accurate catalogs. BLISS is highly extensible, and has the potential to
directly answer downstream scientific questions in addition to producing
probabilistic catalogs.
