Automated Classification of Eclipsing Binary Stars Using Fourier Descriptors and Artificial Neural Networks

Details Automated Classification of Eclipsing Binary Stars Using Fourier Descriptors and Artificial Neural Networks Automated Classification of Eclipsing Binary Stars Using Fourier Descriptors and Artificial Neural Networks

Jan 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011aas...21743321l&link_type=abstract

American Astronomical Society, AAS Meeting #217, #433.21; Bulletin of the American Astronomical Society, Vol. 43, 2011

Computer Science

Databases

Scientific paper

Photometric observation of stars has drastically increased the number of known variable stars to a point where traditional object-by-object analysis is not feasible. Using artificial neural networks for data mining, data reduction and analysis is of great interest to astronomers who now have more data readily available than any person or team could analyze in a lifetime. This poster presents efforts to build a scheme to automatically classify light curves of eclipsing binary stars using Fourier descriptors and artificial neural networks. The raw data was obtained from available public domain databases and a FORTRAN code was written to compute the Fourier descriptors. The Fourier descriptors are presented as inputs to the supervised neural network for training and classifying the light curves. The efficacy of using Fourier descriptors was determined by calculating correlation coefficients using a FORTRAN program. The results of these experiments showed acceptable correlation between the mathematical features represented by the Fourier descriptors and the light curves. The results described here are based on the data available in the public domain.

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