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EDP Sciences, Astronomy & Astrophysics, (556), p. A121, 2013

DOI: 10.1051/0004-6361/201219918

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Identification of metal-poor stars using the artificial neural network

Journal article published in 2013 by Sunetra Giridhar, Aruna Goswami, Andrea Kunder ORCID, S. Muneer, G. Selvakumar
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Identification of metal-poor stars among field stars is extremely useful for studying the structure and evolution of the Galaxy and of external galaxies. We search for metal-poor stars using the artificial neural network (ANN) and extend its usage to determine absolute magnitudes. We have constructed a library of 167 medium-resolution stellar spectra (R ~ 1200) covering the stellar temperature range of 4200 to 8000 K, log g range of 0.5 to 5.0, and [Fe/H] range of -3.0 to +0.3 dex. This empirical spectral library was used to train ANNs, yielding an accuracy of 0.3 dex in [Fe/H], 200 K in temperature, and 0.3 dex in log g. We found that the independent calibrations of near-solar metallicity stars and metal-poor stars decreases the errors in T_eff and log g by nearly a factor of two. We calculated T_eff, log g, and [Fe/H] on a consistent scale for a large number of field stars and candidate metal-poor stars. We extended the application of this method to the calibration of absolute magnitudes using nearby stars with well-estimated parallaxes. A better calibration accuracy for M_V could be obtained by training separate ANNs for cool, warm, and metal-poor stars. The current accuracy of M_V calibration is (+-)0.3 mag. A list of newly identified metal-poor stars is presented. The M_V calibration procedure developed here is reddening-independent and hence may serve as a powerful tool in studying galactic structure. ; Comment: Accepted for publication in Astronomy and Astrophysics, 15 pages, 7 figures