Mapping interstellar dust with Gaussian processes
Andrew C. Miller, Lauren Anderson, Boris Leistedt, John P. Cunningham, David W. Hogg, David M. Blei
The Annals of Applied Statistics · 2022 · Volume 16 · Issue 4
Abstract
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 vantage 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 complication 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 mechanistic model of the Milky Way with millions of stars. ziggy reliably infers the spatial dust map with well-calibrated posterior uncertainties.
Keywords
Astrostatistics, Gaussian process, interstellar dust, stochastic variational inference
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