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.
11th Cambridge Workshop on Cool Stars, Stellar Systems and the Sun
Astronomical Society of the Pacific
Grant or Award Name
U.S. NSF grant AST961814
Scholarly Commons Citation
Snider, S., Qu, Y., Prieto, C. A., Hippel, T. v., Beers, T. C., Sneden, C., Lambert, D. L., & Rossi, S. (1999). (Teff,log g,[Fe/H]) Classification of Low-Resolution Stellar Spectra using Artificial Neural Networks. 11th Cambridge Workshop on Cool Stars, Stellar Systems and the Sun, 223(). Retrieved from https://commons.erau.edu/publication/2069