Automated Classification of Sloan Digital Sky Survey (SDSS) Stellar Spectra using Artificial Neural Networks

Abstract

Automated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated techniques for analysis of such large datasets which are now available to the community. Sloan Digital Sky Survey (SDSS) is one of such surveys releasing massive datasets. We use Probabilistic Neural Network (PNN) for automatic classification of about 5000 SDSS spectra into 158 spectral type of a reference library ranging from O type to M type stars.

27 pages, 11 figures To appear in Astrophys. Space Sci., 2008

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• Comparative performance of some popular ANN algorithms on benchmark and function approximation problems
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• Photo-$z$ with CuBAN$z$: An improved photometric redshift estimator using Clustering aided Back Propagation Neural network