APPROXIMATE INFERENCE FOR CONSTRUCTING ASTRONOMICAL CATALOGS FROM IMAGES

• 成果类型: Article
• 署名作者: Regier, Jeffrey; Miller, Andrew C.; Schlegel, David; Adams, Ryan P.; McAuliffe, Jon D.; Prabhat
• 署名单位: University of California System; University of California Berkeley; University of California System; University of California Berkeley; Columbia University; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Princeton University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/19-AOAS1258
• 发表日期: 2019
• 页码: 1884-1926
• 关键词: digital sky survey; spectroscopic target selection; MODEL

摘要

We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and galaxies. These latent properties are themselves modeled as random. We compare two procedures for posterior inference. One procedure is based on Markov chain Monte Carlo (MCMC) while the other is based on variational inference (VI). The MCMC procedure excels at quantifying uncertainty, while the VI procedure is 1000 times faster. On a supercomputer, the VI procedure efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50 terabytes of images in 14.6 minutes, demonstrating the scaling characteristics necessary to construct catalogs for upcoming astronomical surveys.