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Register a new userStarHorse: A Bayesian tool for determining stellar masses, ages, distances, and extinctions for field stars
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Added by
Anna B\\'arbara de Andrade Queiroz
Publication date
2017
fields
Physics
and research's language is
English
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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.
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We 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 for each star individually. The code was first tested on simulations, successfully recovering input distances to mock stars with <1% bias.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. The code was validated by comparing our distance estimates to parallax measurements from the Hipparcos mission for nearby stars (< 300 pc), to asteroseismic distances of CoRoT red giant stars, and to known distances of well-studied open and globular clusters. The external comparisons confirm that our distances are subject to very small systematic biases with respect to the fundamental Hipparcos scale (+0.4 % for dwarfs, and +1.6% for giants). The typical random distance scatter is 18% for dwarfs, and 26% for giants. For the CoRoT-APOGEE sample, the typical random distance scatter is ~15%, both for the nearby and farther data. Our distances are systematically larger than the CoRoT ones by about +9%, which can mostly be attributed to the different choice of priors. The comparison to known distances of star clusters from SEGUE and APOGEE has led to significant systematic differences for many cluster stars, but with opposite signs, and with substantial scatter. Finally, we tested our distances against those previously determined for a high-quality sample of giant stars from the RAVE survey, again finding a small systematic trend of +5% and an rms scatter of 30%.
For studies of Galactic evolution, the accurate characterization of stars in terms of their evolutionary stage and population membership is of fundamental importance. A standard approach relies on extracting this information from stellar evolution models but requires the effective temperature, surface gravity, and metallicity of a star obtained by independent means. In previous work, we determined accurate effective temperatures and non-LTE logg and [Fe/H] (NLTE-Opt) for a large sample of metal-poor stars, -3<[Fe/H]<-0.5, selected from the RAVE survey. As a continuation of that work, we derive here their masses, ages, and distances using a Bayesian scheme and GARSTEC stellar tracks. For comparison, we also use stellar parameters determined from the widely-used 1D LTE excitation-ionization balance of Fe (LTE-Fe). We find that the latter leads to systematically underestimated stellar ages, by 10-30%, but overestimated masses and distances. Metal-poor giants suffer from the largest fractional distance biases of 70%. Furthermore, we compare our results with those released by the RAVE collaboration for the stars in common (DR3, Zwitter et al. 2010, Seibert et al. 2011). This reveals -400 to +400 K offsets in effective temperature, -0.5 to 1.0 dex offsets in surface gravity, and 10 to 70% in distances. The systematic trends strongly resemble the correlation we find between the NLTE-Opt and LTE-Fe parameters, indicating that the RAVE DR3 data may be affected by the physical limitations of the 1D LTE synthetic spectra. Our results bear on any study, where spectrophotometric distances underlie stellar kinematics. In particular, they shed new light on the debated controversy about the Galactic halo origin raised by the SDSS/SEGUE observations.
We combine high-resolution spectroscopic data from APOGEE-2 Survey Data Release 16 (DR16) with broad-band photometric data from several sources, as well as parallaxes from {it Gaia} Data Release 2 (DR2). Using the Bayesian isochrone-fitting code {tt StarHorse}, we derive distances, extinctions and astrophysical parameters for around 388,815 APOGEE stars, achieving typical distance uncertainties of $sim 6%$ for APOGEE giants, $sim 2%$ for APOGEE dwarfs, as well as extinction uncertainties of $sim 0.07$ mag when all photometric information is available, and $sim 0.17$ mag if optical photometry is missing. {tt StarHorse} uncertainties vary with the input spectroscopic catalogue, with the available photometry, and with the parallax uncertainties. To illustrate the impact of our results, we show that, thanks to {it Gaia} DR2 and the now larger sky coverage of APOGEE-2 (including APOGEE-South), we obtain an extended map of the Galactic plane, providing unprecedented coverage of the disk close to the Galactic mid-plane ($|Z_{Gal}|<1$ kpc) from the Galactic Centre out to $R_{rm Gal}sim 20$ kpc. The improvements in statistics as well as distance and extinction uncertainties unveil the presence of the bar in stellar density, as well as the striking chemical duality in the innermost regions of the disk, now clearly extending to the inner bulge. We complement this paper with distances and extinctions for stars in other public released spectroscopic surveys: 324,999 in GALAH DR2, 4,928,715 in LAMOST DR5, 408,894 in RAVE DR6, and 6,095 in GES DR3
We present a method for obtaining the likelihood function of distance and extinction to a star given its photometry. The other properties of the star (its mass, age, metallicity and so on) are marginalised assuming a simple Galaxy model. We demonstrate that the resulting marginalised likelihood function can be described faithfully and compactly using a Gaussian mixture model. For dust mapping applications we strongly advocate using monochromatic over bandpass extinctions, and provide tables for converting from the former to the latter for different stellar types.
In determining the distances to stars within the Milky Way galaxy, one often uses photometric or spectroscopic parallax. In these methods, the type of each individual star is determined, and the absolute magnitude of that star type is compared with the measured apparent magnitude to determine individual distances. In this article, we define the term statistical photometric parallax, in which statistical knowledge of the absolute magnitudes of stellar populations is used to determine the underlying density distributions of those stars. This technique has been used to determine the density distribution of the Milky Way stellar halo and its component tidal streams, using very large samples of stars from the Sloan Digital Sky Survey. Most recently, the volunteer computing platform MilkyWay@home has been used to find the best fit model parameters for the density of these halo stars.
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