Mapping Interstellar Dust with Gaussian Processes

TL;DR

This paper introduces ziggy, a scalable Gaussian process-based method for mapping interstellar dust using integrated stellar observations, enabling accurate dust distribution inference across millions of stars.

Contribution

The paper presents ziggy, a novel scalable inference approach for Gaussian processes with integrated observations, tailored for large-scale astronomical data.

Findings

Ziggy accurately infers dust maps with calibrated uncertainties.

The method scales efficiently to millions of observations.

Validated on synthetic and real Milky Way data.

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.