Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates

TL;DR

This paper introduces a Bayesian neural flow model to learn a flexible color-magnitude diagram from Gaia data, significantly improving distance estimates and enabling detailed Milky Way structure analysis.

Contribution

The work presents a novel deep neural network approach using normalizing flows to derive a large catalog of photometric distance posteriors from Gaia data, with iterative dust correction and improved accuracy.

Findings

Distance measurement precision improved by over 48%

Enhanced Milky Way disk separation and substructure detection

Model allows marginalization over missing data for broader survey inclusion

Abstract

We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable of learning an arbitrary multi-dimensional probability distribution. We present a catalog of 640M photometric distance posteriors to nearby stars derived from this data-driven model using Gaia DR2 photometry and parallaxes. Dust estimation and dereddening is done iteratively inside the model and without prior distance information, using the Bayestar map. The signal-to-noise (precision) of distance measurements improves on average by more than 48% over the raw Gaia data, and we also demonstrate how the accuracy of distances have improved over other models, especially in the noisy-parallax regime. Applications are discussed, including significantly improved Milky Way disk separation and substructure detection. We conclude with a discussion of future work, which exploits the normalizing flow architecture to allow us to exactly marginalize over missing photometry, enabling the inclusion of many surveys without losing coverage.