Automated Classification of Quasars and Stars

Details Automated Classification of Quasars and Stars Automated Classification of Quasars and Stars

May 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010iaus..267..147z&link_type=abstract

Co-Evolution of Central Black Holes and Galaxies, Proceedings of the International Astronomical Union, IAU Symposium, Volume 267

Computer Science

Databases

Astronomical Databases: Miscellaneous, Catalogs, Methods: Data Analysis, Methods: Statistical

Scientific paper

We investigate selection and weighting of features by applying a random forest algorithm to multiwavelength data. Then we employ a k-nearest neighbor method to distinguish quasars from stars. We then compare the performance of this approach based on all features, weighted features, and selected features. We find that the k-nearest neighbor approach combined with random forests effectively separates quasars from stars.

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