Estimating distances from parallaxes IV: Distances to 1.33 billion stars in Gaia Data Release 2

TL;DR

This paper presents a method to estimate geometric distances for 1.33 billion stars in Gaia DR2 by applying a Bayesian approach with a Galaxy-informed prior, providing reliable distances where simple inversion fails.

Contribution

It introduces a new Bayesian inference procedure that accounts for parallax nonlinearity and asymmetry, producing consistent distances across the entire Gaia DR2 dataset.

Findings

Distances for 1.33 billion stars are provided with confidence intervals.

The catalogue is validated using star clusters.

Distances are independent of stellar physical assumptions.

Abstract

For the vast majority of stars in the second Gaia data release, reliable distances cannot be obtained by inverting the parallax. A correct inference procedure must instead be used to account for the nonlinearity of the transformation and the asymmetry of the resulting probability distribution. Here we infer distances to essentially all 1.33 billion stars with parallaxes published in the second \gaia\ data release. This is done using a weak distance prior that varies smoothly as a function of Galactic longitude and latitude according to a Galaxy model. The irreducible uncertainty in the distance estimate is characterized by the lower and upper bounds of an asymmetric confidence interval. Although more precise distances can be estimated for a subset of the stars using additional data (such as photometry), our goal is to provide purely geometric distance estimates, independent of assumptions about the physical properties of, or interstellar extinction towards, individual stars. We analyse the characteristics of the catalogue and validate it using clusters. The catalogue can be queried on the Gaia archive using ADQL at http://gea.esac.esa.int/archive/ and downloaded from http://www.mpia.de/~calj/gdr2_distances.html .