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From the bulge to the outer disc: StarHorse stellar parameters, distances, and extinctions for stars in APOGEE DR16 and other spectroscopic surveys

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 Publication date 2019
  fields Physics
and research's language is English




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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



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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 present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and returns a posterior probability density function for each stars absolute magnitude. This technique is tailored specifically to data from the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) survey. Because LAMOST obtains roughly 3000 stellar spectra simultaneously within each ~5-degree diameter plate that is observed, we can use the stellar parameters of the observed stars to account for the stellar luminosity function and target selection effects. This removes biasing assumptions about the underlying populations, both due to predictions of the luminosity function from stellar evolution modeling, and from Galactic models of stellar populations along each line of sight. Using calibration data of stars with known distances and stellar parameters, we show that our method recovers distances for most stars within ~20%, but with some systematic overestimation of distances to halo giants. We apply our code to the LAMOST database, and show that the current precision of LAMOST stellar parameters permits measurements of distances with ~40% error bars. This precision should improve as the LAMOST data pipelines continue to be refined.
Combining the precise parallaxes and optical photometry delivered by Gaias second data release (Gaia DR2) with the photometric catalogues of PanSTARRS-1, 2MASS, and AllWISE, we derive Bayesian stellar parameters, distances, and extinctions for 265 million stars brighter than G=18. Because of the wide wavelength range used, our results substantially improve the accuracy and precision of previous extinction and effective temperature estimates. After cleaning our results for both unreliable input and output data, we retain 137 million stars, for which we achieve a median precision of 5% in distance, 0.20 mag in V-band extinction, and 245 K in effective temperature for G<14, degrading towards fainter magnitudes (12%, 0.20 mag, and 245 K at G=16; 16%, 0.23 mag, and 260 K at G=17, respectively). We find a very good agreement with the asteroseismic surface gravities and distances of 7000 stars in the Kepler, the K2-C3, and the K2-C6 fields, with stellar parameters from the APOGEE survey, as well as with distances to star clusters. Our results are available through the ADQL query interface of the Gaia mirror at the Leibniz-Institut f{u}r Astrophysik Potsdam (gaia.aip.de) and as binary tables at data.aip.de. As a first application, in this paper we provide distance- and extinction-corrected colour-magnitude diagrams, extinction maps as a function of distance, and extensive density maps, demonstrating the potential of our value-added dataset for mapping the three-dimensional structure of our Galaxy. In particular, we see a clear manifestation of the Galactic bar in the stellar density distributions, an observation that can almost be considered a direct imaging of the Galactic bar.
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