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Automated Classification Of Stellar Spectra - I. Initial Results With Artificial Neural Networks, Ted Von Hippel, L.J. Storrie-Lombardi, M.C. Storrie-Lombardi, M.J. Irwin
Automated Classification Of Stellar Spectra - I. Initial Results With Artificial Neural Networks, Ted Von Hippel, L.J. Storrie-Lombardi, M.C. Storrie-Lombardi, M.J. Irwin
Publications
We have initiated a project to classify stellar spectra automatically from high-dispersion objective prism plates. The automated technique presented here is a simple back propagation neural network and is based on the visual classification work of Houk. The plate material (Houk’s) is currently being digitized, and contains « 105 stars down to K æ 11 at æ 2-Â resolution from « 3850 to 5150 Â. For this first paper in the series, we report on the results of 575 stars digitized from 6 plates. We find that even with the limited data set now in hand we can determine the …
Automated Classification Of Stellar Spectra: Where Are We Now?, Ted Von Hippel, L.J. Storrie-Lombardi, M.C. Storrie-Lombardi, M.J. Irwin
Automated Classification Of Stellar Spectra: Where Are We Now?, Ted Von Hippel, L.J. Storrie-Lombardi, M.C. Storrie-Lombardi, M.J. Irwin
Publications
We briefly review the work of the past decade on automated classification of stellar spectra and discuss techniques which show particular promise. Emphasis is placed on Artificial Neural Network and Principle Component Analysis based techniques, due both to our greater familiarity with these and to their rising popularity. As an example of the abilities of current techniques we report on our automated classification work based on the visual classifications of N. Houk (Michigan Spectral Catalogue, Vol. 1 - 4, 1975, 1978, 1982, 1988).