Spectro-photometric distances to stars: A general purpose Bayesian approach

Context. Determining distances to individual field stars is a necessary step towards mapping Galactic structure and determining spatial variations in the chemo-dynamical properties of stellar populations in the Milky Way. Aims. In order to provide stellar distance estimates for various spectroscopic surveys, we have developed a code that estimates distances to stars using measured spectroscopic and photometric quantities. We employ a Bayesian approach to build the probability distribution function over stellar evolutionary models given these data, delivering estimates of model parameters (including distances) for each star individually. Our method provides several alternative distance estimates for each star in the output, along with their associated uncertainties. This facilitates the use of our method even in the absence of some measurements. Methods. The code was first tested on simulations, successfully recovering input distances to mock stars with ≲1% bias. We found the uncertainties scale with the uncertainties in the adopted spectro-photometric parameters. The method-intrinsic random distance uncertainties for typical spectroscopic survey measurements amount to around 10% for dwarf stars and 20% for giants, and are most sensitive to the quality of log g measurements. Results. The code was then validated by comparing our distance estimates to parallax measurements from the Hipparco