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Bayesian inference of stellar parameters and interstellar extinction using parallaxes and multiband photometry
Astrometric surveys provide the opportunity to measure the absolutemagnitudes of large numbers of stars, but only if the individualline-of-sight extinctions are known. Unfortunately, extinction is highlydegenerate with stellar effective temperature when estimated frombroad-band optical/infrared photometry. To address this problem, Iintroduce a Bayesian method for estimating the intrinsic parameters of astar and its line-of-sight extinction. It uses both photometry andparallaxes in a self-consistent manner in order to provide anon-parametric posterior probability distribution over the parameters.The method makes explicit use of domain knowledge by employing theHertzsprung-Russell Diagram (HRD) to constrain solutions and to ensurethat they respect stellar physics. I first demonstrate this method byusing it to estimate effective temperature and extinction from BVJHKdata for a set of artificially reddened Hipparcos stars, for whichaccurate effective temperatures have been estimated from high-resolutionspectroscopy. Using just the four colours, we see the expected strongdegeneracy (positive correlation) between the temperature andextinction. Introducing the parallax, apparent magnitude and the HRDreduces this degeneracy and improves both the precision (reduces theerror bars) and the accuracy of the parameter estimates, the latter byabout 35 per cent. The resulting accuracy is about 200 K in temperatureand 0.2 mag in extinction. I then apply the method to estimate theseparameters and absolute magnitudes for some 47 000 F, G, K Hipparcosstars which have been cross-matched with Two-Micron All-Sky Survey(2MASS). The method can easily be extended to incorporate the estimationof other parameters, in particular metallicity and surface gravity,making it particularly suitable for the analysis of the 109stars from Gaia.
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