Mapping Interstellar Dust with Gaussian Processes

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 , a scalable approach to GP inference with integrated observations based on stochastic variational inference. We study on synthetic data and the Ananke dataset, a high-fidelity mechanistic model of the Milky Way with millions of stars.reliably infers the spatial dust map with well-calibrated posterior uncertainties.

Gaussian Process Regression for Astronomical Time Series

“…which can be evaluated in closed form for some standard kernel functions. Another context in which integrated GPs have recently been used for astrophysics (albeit not for time series) is as a model of the Galactic dust distribution (Miller et al 2022).…”

Gaussian Process regression for astronomical time-series

“…which can be evaluated in closed form for some standard kernel functions. Another context where integrated GPs have recently been used for astrophysics (albeit not for time series), is as a model of the Galactic dust distribution (Miller et al 2022).…”