Neural Network Techniques Applied to Low Resolution Spectra of Halo Stars

Details Neural Network Techniques Applied to Low Resolution Spectra of Halo Stars Neural Network Techniques Applied to Low Resolution Spectra of Halo Stars

Dec 1998

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1998aas...193.4409q&link_type=abstract

American Astronomical Society, 193rd AAS Meeting, #44.09; Bulletin of the American Astronomical Society, Vol. 30, p.1316

Computer Science

Databases

2

Scientific paper

Recent large surveys of Galactic halo stars have uncovered kinematically and chemically diverse substructures that contain vital clues to the early evolution of the Galaxy. As extant spectroscopic sample sizes grow into the thousands, traditional star-by-star chemical composition analyses simply will not be able to keep pace. New analytical tools must be found that can attack the large spectroscopic databases in a partially or fully automated fashion (thus providing useful astrophysical data nearly in ``real-time") without sacrificing information content. Here, for a variety of halo-population stars, we present preliminary results of applying an artificial neural network code to low resolution (Requiv lambda /delta lambda ~ 2000) spectra in the wavelength range 3800--5000 Angstroms. We have adapted the back-propogation neural network technique originally devised for stellar spectral classification (von Hippel et al. 1994, MNRAS, 269, 97) to predict effective temperatures, gravities, and overall metallicities from spectra that are being gathered by Beers and colleagues (e.g. 1992, AJ, 103, 1987). First results demonstrate that with properly trained neural networks, the T_eff and [Fe/H] values may be predicted to typically +/-50K and +/-0.2 dex, respectively, for stars with well-determined parameters from the literature. We will discuss these trends, as well as additional studies of the application to log g predictions, and experiments that focus in on individual abundance ratios (principally [C/Fe], [Ba/Fe], and [Sr/Fe]).

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