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Title:
Automated derivation of stellar atmospheric parameters and chemical abundances: the MATISSE algorithm
Authors:
Recio-Blanco, A.; Bijaoui, A.; de Laverny, P.
Affiliation:
AA(Département Cassiopée, UMR 6202, Observatoire de la Côte d'Azur, BP 4229, F-06304 Nice Cedex 4, France; ), AB(Département Cassiopée, UMR 6202, Observatoire de la Côte d'Azur, BP 4229, F-06304 Nice Cedex 4, France; ), AC(Département Cassiopée, UMR 6202, Observatoire de la Côte d'Azur, BP 4229, F-06304 Nice Cedex 4, France; )
Publication:
Monthly Notices of the Royal Astronomical Society, Volume 370, Issue 1, pp. 141-150. (MNRAS Homepage)
Publication Date:
07/2006
Origin:
MNRAS
MNRAS Keywords:
methods: data analysis: techniques: spectroscopic: stars: abundances: stars: fundamental parameters: Galaxy: stellar content, methods: data analysis, techniques: spectroscopic, stars: abundances, stars: fundamental parameters, Galaxy: stellar content
DOI:
10.1111/j.1365-2966.2006.10455.x
Bibliographic Code:
2006MNRAS.370..141R

Abstract

We present an automated procedure for the derivation of atmospheric parameters (Teff, log g, [M/H]) and individual chemical abundances from stellar spectra. The MATrix Inversion for Spectral SynthEsis (MATISSE) algorithm determines a basis, Bθ(λ), allowing the derivation of a particular stellar parameter θ by projection of an observed spectrum. The Bθ(λ) function is determined from an optimal linear combination of theoretical spectra and it relates, in a quantitative way, the variations in the spectrum flux with variations in θ. An application of this method to the Gaia Radial Velocity Spectrograph spectral range is described, together with its performances for different types of stars of various metallicities. Blind tests with synthetic spectra of randomly selected parameters and observed input spectra are also presented. The method gives rapid, accurate and stable results and it can be efficiently applied to the study of stellar populations through the analysis of large spectral data sets, including moderate to low signal-to-noise ratio spectra.

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