Large-scale three-dimensional Gaussian process extinction mapping

TL;DR

This paper introduces a scalable and efficient Gaussian process-based method for three-dimensional dust extinction mapping in the Milky Way, enabling analysis of large stellar surveys like Gaia.

Contribution

It combines Expectation Propagation and sparse matrix approximations to significantly accelerate Gaussian process dust mapping, making large-scale Galactic studies feasible.

Findings

Algorithm is fast, accurate, and precise with simulated Gaia data.

Method scales to large datasets like the Gaia catalogue.

Demonstrates practical application of Gaussian processes in Galactic ISM mapping.

Abstract

Gaussian processes are the ideal tool for modelling the Galactic ISM, combining statistical flexibility with a good match to the underlying physics. In an earlier paper we outlined how they can be employed to construct three-dimensional maps of dust extinction from stellar surveys. Gaussian processes scale poorly to large datasets though, which put the analysis of realistic catalogues out of reach. Here we show how a novel combination of the Expectation Propagation method and certain sparse matrix approximations can be used to accelerate the dust mapping problem. We demonstrate, using simulated Gaia data, that the resultant algorithm is fast, accurate and precise. Critically, it can be scaled up to map the Gaia catalogue.