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

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

2002-06-05

arxiv.org/abs/astro-ph/0206085v1

Astronomy and Astrophysics

Astrophysics

10 pages, 4 figures; A&A in press

Scientific paper

10.1051/0004-6361:20020830

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 (Teff, log g, [Fe/H]) achievable in the HES for stars in the effective temperature range 520010 (typically corresponding to B_J<16.5). The accuracies in log g and [Fe/H] are better than 0.68dex 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.

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