Statistical methods of automatic spectral classification and their application to the Hamburg/ESO Survey

Free access article

Issue		A&A Volume 391, Number 1, August III 2002


Page(s)		397 - 406
Section		Instruments, observational techniques and data processing
DOI		http://dx.doi.org/10.1051/0004-6361:20020830

A&A 391, 397-406 (2002)
DOI: 10.1051/0004-6361:20020830

Statistical methods of automatic spectral classification and their application to the Hamburg/ESO Survey

N. Christlieb¹, L. Wisotzki² and G. Graßhoff³

¹ Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112, 21029 Hamburg, Germany
    e-mail: nchristlieb@hs.uni-hamburg.de
² Institut für Physik, Universität Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany
    e-mail: lutz@astro.physik.uni-potsdam.de
³ Institut für Philosophie der Universität Bern, Länggassstrasse 49a, 3012 Bern, Switzerland
    e-mail: gerd.grasshoff@philo.unibe.ch

(Received 10 April 2002 / Accepted 4 June 2002 )

Abstract
We employ classical statistical methods of multivariate classification for the exploitation of the stellar content of the Hamburg/ESO objective prism survey (HES). In a simulation study we investigate the precision of a three-dimensional classification ( $T_{\mbox{\scriptsize eff}}$ , $\log g$ , [Fe/H]) achievable in the HES for stars in the effective temperature range $5200~\mbox{K}<T_{\mbox{\scriptsize eff}}<6800~\mbox{K}$ , using Bayes classification. The accuracy in temperature determination is better than 400 K for HES spectra with S/N>10 (typically corresponding to B_J<16.5). The accuracies in $\log g$ and [Fe/H] are better than 0.68 dex in the same S/N range. These precisions allow for a very efficient selection of metal-poor stars in the HES. We present a minimum cost rule for compilation of complete samples of objects of a given class, and a rejection rule for identification of corrupted or peculiar spectra. The algorithms we present are being used for the identification of other interesting objects in the HES data base as well, and they are applicable to other existing and future large data sets, such as those to be compiled by the DIVA and GAIA missions.

Key words: surveys -- methods: data analysis -- stars: fundamental parameters -- Galaxy: halo

Offprint request: N. Christlieb, nchristlieb@hs.uni-hamburg.de

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