New generation large-aperture telescopes, multi-object spectrographs, and
large format detectors are making it possible to acquire very large samples of
stellar spectra rapidly. In this context, traditional star-by-star
spectroscopic analysis are no longer practical. New tools are required that are
capable of extracting quickly and with reasonable accuracy important basic
stellar parameters coded in the spectra. Recent analyses of Artificial Neural
Networks (ANNs) applied to the classification of astronomical spectra have
demonstrated the ability of this concept to derive estimates of temperature and
luminosity. We have adapted the back-propagation ANN technique developed by von
Hippel et al. (1994) to predict effective temperatures, gravities and overall
metallicities from spectra with resolving power ~ 2000 and low signal-to-noise
ratio. We show that ANN techniques are very effective in executing a
three-parameter (Teff,log g,[Fe/H]) stellar classification. The preliminary
results show that the technique is even capable of identifying outliers from
the training sample.Comment: 6 pages, 3 figures (5 files); to appear in the proceedings of the
11th Cambridge Workshop on Cool Stars, Stellar Systems and the Sun, held on
Tenerife (Spain), October 1999; also available at http://hebe.as.utexas.ed
