Sequential Monte Carlo for Detecting and Deblending Objects in Astronomical Images

Many of the objects imaged by the forthcoming generation of astronomical surveys will overlap visually. These objects are known as blends. Distinguishing and characterizing blended light sources is a challenging task, as there is inherent ambiguity in the type, position, and properties of each source. We propose SMC-Deblender, a novel approach to probabilistic astronomical cataloging based on sequential Monte Carlo (SMC). Given an image, SMC-Deblender evaluates catalogs with various source counts by partitioning the SMC particles into blocks. With this technique, we demonstrate that SMC can be a viable alternative to existing deblending methods based on Markov chain Monte Carlo and variational inference. In experiments with ambiguous synthetic images of crowded starfields, SMC-Deblender accurately detects and deblends sources, a task which proves infeasible for Source Extractor, a widely used non-probabilistic cataloging program.