StarHorse : a Bayesian tool for determining stellar masses, ages, distances, and extinctions for field stars

By A. Queiroz, Friedrich Anders, B. Santiago, C. Chiappini, Matthias Steinmetz, M. D. Ponte, Keivan G. Stassun, Luiz Nicolaci da Costa, Marcio A. G. Maia, Timothy C. Beers and 5 more. 2017

135 citations recorded by the source corpus.

Understanding the formation and evolution of our Galaxy requires accurate distances, ages and chemistry for large populations of field stars. Here we present several updates to our spectro-photometric distance code, that can now also be used to estimate ages, masses, and extinctions for individual stars. Given a set of measured spectro-photometric parameters, we calculate the posterior probability distribution over a given grid of stellar evolutionary models, using flexible Galactic stellar-population priors. The code (called {\tt StarHorse}) can acommodate different observational datasets, prior options, partially missing data, and the inclusion of parallax information into the estimated probabilities. We validate the code using a variety of simulated stars as well as real stars with parameters determined from asteroseismology, eclipsing binaries, and isochrone fits to star clusters. Our main goal in this validation process is to test the applicability of the code to field stars with known {\it Gaia}-like parallaxes. The typical internal precision (obtained from realistic simulations of an APOGEE+Gaia-like sample) are $\simeq 8\%$ in distance, $\simeq 20\%$ in age,$\simeq 6\ %$ in mass, and $\simeq 0.04$ mag in $A_V$. The median external precision (derived from comparisons with earlier work for real stars) varies with the sample used, but lies in the range of $\simeq [0,2]\%$ for distances, $\simeq [12,31]\%$ for ages, $\simeq [4,12]\%$ for masses, and $\simeq 0.07$ mag for $A_V$. We provide StarHorse distances and extinctions for the APOGEE DR14, RAVE DR5, GES DR3 and GALAH DR1 catalogues.

Subfields: Astronomy and Astrophysics, Instrumentation

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Citations in the corpus

Yule-Simpson’s paradox in Galactic Archaeology (2019, methodology): “…using AMBRE:HARPS (de Laverny et al. 2013; De Pascale et al. 2014; Hayden et al. 2017) and HARPS-GTO data (Adibekyan et al. 2012; Delgado Mena et al. 2017) combined with stellar ages computed with the StarHorse code (Santiago et al. 2016; Queiroz et al. 2018), as described in Anders et al. (2018).”
Estimating Distance from Parallaxes. IV. Distances to 1.33 Billion Stars in Gaia Data Release 2 (2018, methodology): “…combining parallaxes with other information to improve distance measures include Schönrich & Bergemann (2014), Leistedt & Hogg (2017), O’Malley et al. (2017), Anderson et al. (2017), Queiroz et al. (2018), Cantat-Gaudin et al. (2018), Coronado et al. (2018), and Fouesneau et al. (in preparation).”
Estimating stellar birth radii and the time evolution of Milky Way’s ISM metallicity gradient (2018, methodology): “Ages and distances were estimated with the StarHorse code (Santiago et al. 2016; Queiroz et al. 2018), as described in Anders et al. (2018).”
A 3D Dust Map Based on Gaia, Pan-STARRS 1, and 2MASS (2019, methodology): “In order to validate the accuracy of our stellar reddening estimates, we compare them to those estimated by Queiroz et al. (2018) (hereafter, “StarHorse”), which are based on APOGEE DR14 spectroscopy (Abolfathi et al. 2018), 2MASS JHKs photometry, APASS DR9 BVg′r′i′ photometry (Henden & Munari…”
Photo-astrometric distances, extinctions, and astrophysical parameters for Gaia DR2 stars brighter than G = 18 (2019, methodology): “Since Queiroz et al. (2018), the StarHorse code was migrated python 2.7 to python 3.6 and runs on the newton cluster at the Leibniz-Institut für Astrophysik Potsdam (AIP).”