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，恒星与星系常出现视觉重叠。去混叠是指区分并表征巡天图像中独立光源的任务。我们提出StarNet，一种基于贝叶斯方法的拥挤星场天文图像光源去混叠算法。StarNet利用了变分推断领域的最新进展，包括摊销变分分布及以正向KL散度期望为目标的优化函数。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对比方法：采用MCMC进行推断的概率编目法（PCAT）及SDSS用于去混叠的软件流水线DAOPHOT。此外，摊销式推断方法赋予StarNet必要的缩放特性，使其能对现代天文巡天数据进行贝叶斯推断。