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Automatic Classification of Subdwarf Spectra using a Neural Network

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Abstract

We apply a multilayer feed-forward back propagation artificial neural network to a sample of 380 subdwarf spectra classified by Drilling et al. (Drilling, J.S., Moehler, S., Jeffery, C.S., Heber, U., and Napiwotzki, R.: in press in: R. Gray (ed.), Probing the Personalities of Stars and Galaxies), showing that it is possible to use this technique on large sets of spectra and obtain classifications in good agreement with the standard. We briefly investigate the impact of training set size, showing that large training sets do not necessarily perform significantly better than small sets.

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Authors and Affiliations

  1. Armagh Observatory, College Hill, Armagh, BT61 9DG, N. Ireland

    C. Winter & C.S. Jeffery

  2. Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, 70803

    J.S. Drilling

Authors
  1. C. Winter
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  2. C.S. Jeffery
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  3. J.S. Drilling
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Winter, C., Jeffery, C. & Drilling, J. Automatic Classification of Subdwarf Spectra using a Neural Network. Astrophys Space Sci 291, 375–378 (2004). https://doi.org/10.1023/B:ASTR.0000044347.51059.67

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  • DOI: https://doi.org/10.1023/B:ASTR.0000044347.51059.67

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