Submitting Campus

Daytona Beach


Physical Sciences

Document Type


Publication/Presentation Date



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.

Publication Title

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

Additional Information

Dr. von Hippel was not affiliated with Embry-Riddle Aeronautical University at the time this paper was published.