Unbiased TGAS$\times$LAMOST distances and the role of binarity

TL;DR

This paper introduces a probabilistic method for estimating spectroscopic distances to main sequence stars that accounts for parallax uncertainties and binarity, significantly improving distance accuracy and orbital parameter estimates.

Contribution

The novel approach explicitly models individual parallax errors and binary fractions, providing more accurate spectroscopic distances for large stellar samples.

Findings

Achieves ~6% distance uncertainties for single stars.

Reduces orbital action uncertainties by a factor of five.

Outperforms Gaia DR2 distances for distant, faint stars.

Abstract

Spectrophotometric distances to stars observed by large spectroscopic surveys offer a crucial complement to parallax distances that remain very important also after the future Gaia data releases. Here we present a probabilistic approach to modeling spectroscopic information for a subset of 4,000 main sequence stars with good parallaxes ($\sigma_\varpi/\varpi<0.1$) from the LAMOST $\times$ TGAS $\times$ 2MASS cross-match, yielding a precise spectroscopic distance estimator with uncertainties of $\sim$6% for single stars. Unlike previous approaches to this problem, we explicitly account for the individual parallax uncertainties in the model building and fully incorporate the fraction of near-equal binaries of main sequence stars, which would lead to biased distance estimates if neglected. Using this model, we estimate the distance for all (150,000) main sequence stars from LAMOST Data Release 5, without parallax information. As an application, we compute their orbital actions, where our more precise distances result in 5 times smaller action uncertainties. This illustrates how future studies of the Milky Way's orbital structure can benefit from using our model. For the fainter and more distant stars of most current spectroscopic surveys, an approach such as the one presented in this work will deliver better distances than Gaia Data Release 2.