2014MNRAS.438.2938H

We present a method to simultaneously infer the interstellar extinction parameters A0 and R0, stellar effective temperature Teff and distance modulus µ in a Bayesian framework. Using multiband photometry from Sloan Digital Sky Survey and UKIRT Infrared Deep Sky Survey, we train a forward model to emulate the colour change due to physical properties of stars and the interstellar medium for temperatures from 4000 to 9000K and extinctions from 0 to 5mag. We introduce a Hertzsprung-Russell diagram prior to account for physical constraints on the distribution of stars in the temperature-absolute magnitude plane. This allows us to infer distances probabilistically. Influences of colour information, priors and model parameters are explored. Residual mean absolute errors (MAEs) on a set of objects for extinction and temperature are 0.2mag and 300K, respectively, for R0 fixed to 3.1. For variable R0, we obtain MAEs of 0.37mag, 412.9K and 0.74 for A0, Teff and R0, respectively. Distance moduli are accurate to approximately 2mag. Quantifying the precisions of individual parameter estimates with 68percent confidence interval of the posterior distribution, we obtain 0.05mag, 66K, 2mag and 0.07 for A0, Teff, µ and R0, respectively, although we find that these underestimate the accuracy of the model. We produce two-dimensional maps in extinction and R0 that are compared to previous work. Furthermore, we incorporate the inferred distance information to compute fully probabilistic distance profiles for individual lines of sight. The individual stellar astrophysical parameter (AP) estimates, combined with inferred 3D information, will make possible many Galactic science and modelling applications. Adapting our method to work with other surveys, such as Pan-STARRS and Gaia, will allow us to probe other regions of the Galaxy.