TY - JOUR
T1 - (Teff,log g,[Fe/H]) Classification of Low-Resolution Stellar Spectra using Artificial Neural Networks
AU - Snider, Shawn
AU - Qu, Yuan
AU - Prieto, Carlos Allende
AU - Hippel, Ted von
AU - Beers, Timothy C.
AU - Sneden, Christopher
AU - Lambert, David L.
AU - Rossi, Silvia
PY - 1999/12/19
Y1 - 1999/12/19
N2 - 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 λ/δλ ≃ 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.
AB - 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 λ/δλ ≃ 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.
KW - stellar spectra classification
KW - Artificial Neural Networks
UR - https://commons.erau.edu/publication/2069
M3 - Article
VL - 223
JO - 11th Cambridge Workshop on Cool Stars, Stellar Systems and the Sun
JF - 11th Cambridge Workshop on Cool Stars, Stellar Systems and the Sun
ER -