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

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-eff, logg, [Fe/H]) achievable in the HES for stars in the effective temperature range 5200 K < T-eff < 6800 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.