(Teff,log g,[Fe/H]) Classification of Low-Resolution Stellar Spectra using Artificial Neural Networks

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dc.creatorSnider, Shawn
dc.creatorQu, Yuan
dc.creatorPrieto, Carlos Allende
dc.creatorvon Hippel, Ted
dc.creatorBeers, Timothy C.
dc.creatorSneden, Chistopher
dc.creatorLambert, David L.
dc.date1999-12-19
dc.date.accessioned2026-07-07T02:35:06Z
dc.date.available2026-07-07T02:35:06Z
dc.descriptionNew 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.
dc.description6 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.edu
dc.identifierhttps://arxiv.org/abs/astro-ph/9912404
dc.identifierhttp://arxiv.org/abs/astro-ph/9912404
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/15492
dc.subjectAstrophysics
dc.title(Teff,log g,[Fe/H]) Classification of Low-Resolution Stellar Spectra using Artificial Neural Networks
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