PREPRINT
C752BBAB-6F1A-4CE2-BE90-BB97BC11BC93

Scalable Bayesian Inference for Detection and Deblending in Astronomical Images

Derek Hansen, Ismael Mendoza, Runjing Liu, Ziteng Pang, Zhe Zhao, Camille Avestruz, Jeffrey Regier
arXiv:2207.05642

Submitted on 12 July 2022

Abstract

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.

Preprint

Comment: Accepted to the ICML 2022 Workshop on Machine Learning for Astrophysics. 5 pages, 2 figures

Subjects: Astrophysics - Instrumentation and Methods for Astrophysics; Statistics - Applications; Statistics - Machine Learning

URL: https://arxiv.org/abs/2207.05642