Three‐dimensional Spectral Classification of Low‐Metallicity Stars Using Artificial Neural Networks

We explore the application of artificial neural networks (ANNs) for theestimation of atmospheric parameters (Teff, logg, and [Fe/H]) for Galactic F-and G-type stars. The ANNs are fed with medium-resolution (~ 1-2 A) nonflux-calibrated spectroscopic observations. From a sample of 279 stars withprevious high-resolution determinations of metallicity, and a set of (external)estimates of temperature and surface gravity, our ANNs are able to predict Teffwith an accuracy of ~ 135-150 K over the range 4250 <= Teff <= 6500 K, loggwith an accuracy of ~ 0.25-0.30 dex over the range 1.0 <= logg <= 5.0 dex, and[Fe/H] with an accuracy ~ 0.15-0.20 dex over the range -4.0 <= [Fe/H] <= +0.3.Such accuracies are competitive with the results obtained by fine analysis ofhigh-resolution spectra. It is noteworthy that the ANNs are able to obtainthese results without consideration of photometric information for these stars.We have also explored the impact of the signal-to-noise ratio (S/N) on thebehavior of ANNs, and conclude that, when analyzed with ANNs trained on spectraof commensurate S/N, it is possible to extract physical parameter estimates ofsimilar accuracy with stellar spectra having S/N as low as 13. Taken together,these results indicate that the ANN approach should be of primary importancefor use in present and future large-scale spectroscopic surveys.Comment: 51 pages, 11 eps figures, uses aastex; to appear in Ap