Automatic Spectral Classification

Abstract

In this paper we report on a joint research project between astronomers and philosophers of science. The philosophical and the astronomical goal are described and the astronomical background is shortly reviewed. We present the current status of our development of methods for tackling the relevant classification problems, i.e.: (1) application of Bayes’ decision rule for “simple” classification of all spectra in the data base; (2) minimum cost rule classification for compilation of complete samples of rare stellar objects and (3) Bayes classification with application of an atypicality index reject criterion for the detection of non-stellar spectra. We report on the discovery of an extremely metal poor halo star by application of method (2) to a small fraction of our data. A method for adequate handling of low signal-to-noise ratio spectra is presented. The classification methods presented are currently applied to a large data base of digital spectra.