MAPPING INTERSTELLAR DUST WITH GAUSSIAN PROCESSES

• 成果类型: Article
• 署名作者: Miller, Andrew C.; Anderson, Lauren; Leistedt, Boris; Cunningham, John P.; Hogg, David W.; Blei, David M.
• 署名单位: Columbia University; Columbia University; New York University; Simons Foundation; Flatiron Institute
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1608
• 发表日期: 2022
• 页码: 2672-2692
• 关键词: spiral structure; inference; models; BAYES

摘要

Interstellar dust corrupts nearly every stellar observation and accounting for it is crucial to measuring physical properties of stars. We model the dust distribution as a spatially varying latent field with a Gaussian process (GP) and develop a likelihood model and inference method that scales to millions of astronomical observations. Modeling interstellar dust is complicated by two factors. The first is integrated observations. The data come from a van-tage point on Earth, and each observation is an integral of the unobserved function along our line of sight, resulting in a complex likelihood and a more difficult inference problem than in classical GP inference. The second com-plication is scale; stellar catalogs have millions of observations. To address these challenges, we develop ZIGGY, a scalable approach to GP inference with integrated observations based on stochastic variational inference. We study ZIGGY on synthetic data and the Ananke dataset, a high-fidelity mech-anistic model of the Milky Way with millions of stars. ZIGGY reliably infers the spatial dust map with well-calibrated posterior uncertainties.