Improving Gaia Parallax Precision with a Data-driven Model of Stars

Abstract

Abstract Converting a noisy parallax measurement into a posterior belief over distance requires inference with a prior. Usually, this prior represents beliefs about the stellar density distribution of the Milky Way. However, multiband photometry exists for a large fraction of the Gaia-TGAS Catalog and is incredibly informative about stellar distances. Here, we use 2MASS colors for 1.4 million TGAS stars to build a noise-deconvolved empirical prior distribution for stars in color–magnitude space. This model contains no knowledge of stellar astrophysics or the Milky Way but is precise because it accurately generates a large number of noisy parallax measurements under an assumption of stationarity; that is, it is capable of combining the information from many stars. We use the Extreme Deconvolution (XD) algorithm—which is an empirical-Bayes approximation to a full-hierarchical model of the true parallax and photometry of every star—to construct this prior. The prior is combined with a TGAS likelihood to infer a precise photometric-parallax estimate and uncertainty (and full posterior) for every star. Our parallax estimates are more precise than the TGAS catalog entries by a median factor of 1.2 (14% are more precise by a factor >2) and they are more precise than the previous Bayesian distance estimates that use spatial priors. We validate our parallax inferences using members of the Milky Way star cluster M67, which is not visible as a cluster in the TGAS parallax estimates but appears as a cluster in our posterior parallax estimates. Our results, including a parallax posterior probability distribution function for each of 1.4 million TGAS stars, are available in companion electronic tables.