Bayesian inference of stellar parameters and interstellar extinction using parallaxes and multiband photometry
C.A.L. Bailer-Jones (MPIA, Heidelberg)

TL;DR
This paper introduces a Bayesian method combining photometry and parallaxes, constrained by stellar physics, to accurately estimate stellar parameters and interstellar extinction, significantly reducing degeneracy and improving precision.
Contribution
It presents a novel Bayesian approach that jointly estimates stellar parameters and extinction using photometry, parallaxes, and the Hertzsprung--Russell Diagram, enhancing accuracy over traditional methods.
Findings
Reduces temperature-extinction degeneracy by 35%
Achieves ~200K temperature accuracy and 0.2mag extinction accuracy
Successfully applied to 47,000 Hipparcos stars with improved estimates
Abstract
Astrometric surveys provide the opportunity to measure the absolute magnitudes of large numbers of stars, but only if the individual line-of-sight extinctions are known. Unfortunately, extinction is highly degenerate with stellar effective temperature when estimated from broad band optical/infrared photometry. To address this problem, I introduce a Bayesian method for estimating the intrinsic parameters of a star and its line-of-sight extinction. It uses both photometry and parallaxes in a self-consistent manner in order to provide a non-parametric posterior probability distribution over the parameters. The method makes explicit use of domain knowledge by employing the Hertzsprung--Russell Diagram (HRD) to constrain solutions and to ensure that they respect stellar physics. I first demonstrate this method by using it to estimate effective temperature and extinction from BVJHK data for a…
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