Automated Classification Techniques for Large Spectroscopic Surveys

Abstract

With the onset of large, systematically selected spectroscopic surveys we have the opportunity to understand the distribution and evolution of galaxies in terms of the mix of their spectral population. In this proceedings we describe a series of statistical techniques, ranging from Karhunen-Lo'eve transform to wavelet transforms, that are being applied to the spectra from the Sloan Digital Sky Survey in order to define a statistically robust and objective spectral classification scheme. The approach we describe for the classification of galaxy, stellar and QSO spectra is at the interface of astrophysics, statistics and computer science. To enable these techniques to be applied and interpreted successfully requires both robust statistical inference together with fast and efficient computer algorithms. Combining these three disciplines we can fully exploit the wealth of physical information present within the SDSS spectra.

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