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Cross-checking reliability of some available stellar spectral libraries using artificial neural networks

Published online by Cambridge University Press:  01 December 2006

Ranjan Gupta ,
S. Jotin Singh  and
Harinder P. Singh
Ranjan Gupta
Affiliation:
IUCAA, Post Bag 4, Ganeshkhind, Pune 411 007, India
S. Jotin Singh
Affiliation:
Department of Physics & Astrophysics, University of Delhi, Delhi – 110 007, India
Harinder P. Singh
Affiliation:
Department of Physics & Astrophysics, University of Delhi, Delhi – 110 007, India
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Abstract

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Cross-checking the reliability of various stellar spectral databases is an important and desirable exercise. Since number of stars in various databases have no known spectral types and some of the libraries do not have complete coverage resulting in gaps. We use an automated classification scheme based on Artificial Neural Networks (ANN) to cross-classify stars in the Indo-US stellar spectral library (Valdes et al. 2004), JHC (Jacoby, Hunter & Christian 1984), ELODIE spectra (Moultaka et al. 2004) and STELIB (Le Borgne et al. 2003). We have also examined the effects of over-training and over-fitting on the classification efficiency of a Neural Network. It is hoped that such a automated data analysis and validation technique will be useful in the future.

Keywords

StarsCatalogsArtificial Neural NetworksMethods: Statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 2 , Symposium S241: Stellar Populations as Building Blocks of Galaxies , December 2006 , pp. 93 - 94
Copyright
Copyright © International Astronomical Union 2007

References

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