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.

翻译：在天文巡天图像中，恒星和星系常常存在视觉重叠。去混叠是区分并表征巡天图像中单个光源的任务。我们提出StarNet，一种用于解混拥挤星场天文图像中光源的贝叶斯方法。StarNet利用了变分推理的最新进展，包括摊销变分分布以及针对前向KL散度期望值的优化目标。在针对M2球状星团的SDSS图像实验中，StarNet的精度显著优于两种竞争方法：使用MCMC进行推理的概率编目法（PCAT），以及SDSS用于去混叠的软件流水线DAOPHOT。此外，摊销推理方法使StarNet具备对现代天文巡天数据进行贝叶斯推理所需的扩展特性。