拥挤星场去混合的变分推断
Variational Inference for Deblending Crowded Starfields
AI总结：
提出贝叶斯去混合方法StarNet，利用摊销变分推断和前向KL散度目标，在SDSS M2球状星团图像上显著优于PCAT和DAOPHOT，并具备处理现代巡天数据的扩展能力。
摘要：
在天文巡天采集的图像中，恒星和星系常常在视觉上重叠。去混合是区分并表征巡天图像中单个光源的任务。我们提出StarNet，一种用于在拥挤星场天文图像中对光源进行去混合的贝叶斯方法。StarNet利用了变分推断的最新进展，包括摊销变分分布以及针对前向KL散度期望的优化目标。在我们使用SDSS的M2球状星团图像进行的实验中，StarNet比两种竞争方法准确得多：概率编目（PCAT），一种使用MCMC进行推断的方法，以及DAOPHOT，SDSS用于去混合的软件流程。此外，摊销推断方法赋予StarNet必要的扩展特性，使其能够对现代天文巡天进行贝叶斯推断。
英文摘要：
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.
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