Mapping Interstellar Dust with Gaussian Processes
  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.
