Estimating distances from parallaxes. III. Distances of two million stars in the Gaia DR1 catalogue

TL;DR

This paper estimates distances for two million stars in Gaia DR1 using Bayesian methods with different priors, validating results against Cepheid distances and providing a publicly available catalog.

Contribution

It introduces a new distance estimation method for Gaia DR1 stars using two priors, comparing their effectiveness and providing a large, validated distance catalog.

Findings

Milky Way prior outperforms exponential prior for nearby stars

Distance estimates are more accurate within 2 kpc

Catalog is publicly available for individual star distances

Abstract

We infer distances and their asymmetric uncertainties for two million stars using the parallaxes published in the Gaia DR1 (GDR1) catalogue. We do this with two distance priors: A minimalist, isotropic prior assuming an exponentially decreasing space density with increasing distance, and an anisotropic prior derived from the observability of stars in a Milky Way model. We validate our results by comparing our distance estimates for 105 Cepheids which have more precise, independently estimated distances. For this sample we find that the Milky Way prior performs better (the RMS of the scaled residuals is 0.40) than the exponentially decreasing space density prior (RMS is 0.57), although for distances beyond 2 kpc the Milky Way prior performs worse, with a bias in the scaled residuals of -0.36 (vs. -0.07 for the exponentially decreasing space density prior). We do not attempt to include the photometric data in GDR1 due to the lack of reliable colour information. Our distance catalogue is available at http://www.mpia.de/homes/calj/tgas distances/main.html as well as at CDS. This should only be used to give individual distances. Combining data or testing models should be done with the original parallaxes, and attention paid to correlated and systematic uncertainties.