Volume 585, January 2016
|Number of page(s)||22|
|Section||Numerical methods and codes|
|Published online||23 December 2015|
Stellar parametrization from Gaia RVS spectra
Laboratoire Lagrange (UMR 7293), Université de Nice Sophia Antipolis, CNRS,
Observatoire de la Côte d’Azur, CS
2 Instituto de Astrofísica de Canarias, 38205, La Laguna, Tenerife, Spain
3 Universidad de La Laguna, Dept. de Astrofísica, 38206, La Laguna, Tenerife, Spain
4 Departamento de Tecnologías de la Información y de las Comunicaciones, Universidade da Coruña, 15071 A Coruña, Spain
5 Departamento de Ciencias de la Navegación y de la Tierra, Universidade da Coruña, 15011 A Coruña, Spain
6 Geneva Observatory, University of Geneva, 51 ch. des Maillettes, 1290 Versoix, Switzerland
Received: 19 September 2014
Accepted: 30 September 2015
Context. Among the myriad of data collected by the ESA Gaia satellite, about 150 million spectra will be delivered by the Radial Velocity Spectrometer (RVS) for stars as faint as GRVS~ 16. A specific stellar parametrization will be performed on most of these RVS spectra, i.e. those with enough high signal-to-noise ratio (S/N), which should correspond to single stars that have a magnitude in the RVS band brighter than ~14.5. Some individual chemical abundances will also be estimated for the brightest targets.
Aims. We describe the different parametrization codes that have been specifically developed or adapted for RVS spectra within the GSP-Spec working group of the analysis consortium. The tested codes are based on optimisation (FERRE and GAUGUIN), projection (MATISSE), or pattern-recognition methods (Artificial Neural Networks). We present and discuss each of their expected performances in the recovered stellar atmospheric parameters (effective temperature, surface gravity, overall metallicity) for B- to K-type stars. The performances for determining of [α/Fe] ratios are also presented for cool stars.
Methods. Each code has been homogeneously tested with a large grid of RVS simulated synthetic spectra of BAFGK-spectral types (dwarfs and giants), with metallicities varying from 10-2.5 to 10+ 0.5 the solar metallicity, and taking variations of ±0.4 dex in the composition of the α-elements into consideration. The tests were performed for S/N ranging from ten to 350.
Results. For all the stellar types we considered, stars brighter than GRVS~ 12.5 are very efficiently parametrized by the GSP-Spec pipeline, including reliable estimations of [α/Fe]. Typical internal errors for FGK metal-rich and metal-intermediate stars are around 40 K in Teff, 0.10 dex in log(g), 0.04 dex in [M/H], and 0.03 dex in [α/Fe] at GRVS = 10.3. They degrade to 155 K in Teff, 0.15 dex in log(g), 0.10 dex in [M/H], and 0.1 dex in [α/Fe] at GRVS~ 12. Similar accuracies in Teff and [M/H] are found for A-type stars, while the log(g) derivation is more accurate (errors of 0.07 and 0.12 dex at GRVS = 12.6 and 13.4, respectively). For the faintest stars, with GRVS≳ 13−14, a Teff input from the spectrophotometric-derived parameters will allow the final GSP-Spec parametrization to be improved.
Conclusions. The reported results, while neglecting possible mismatches between synthetic and real spectra, show that the contribution of the RVS-based stellar parameters will be unique in the brighter part of the Gaia survey, which allows for crucial age estimations and accurate chemical abundances. This will constitute a unique and precious sample, providing many pieces of the Milky Way history puzzle with unprecedented precision and statistical relevance.
Key words: stars: fundamental parameters / Galaxy: stellar content
© ESO, 2015
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