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Automated Classification of Quasars and Stars

Published online by Cambridge University Press:  03 June 2010

Yanxia Zhang ,
Yongheng Zhao  and
Hongwen Zheng
Yanxia Zhang
Affiliation:
National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China Email: zyx@lamost.org
Yongheng Zhao
Affiliation:
National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China Email: zyx@lamost.org
Hongwen Zheng
Affiliation:
North China Electronic Power University, Beijing, 102206, China Email: zyx300@sohu.com
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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.

Keywords

astronomical databases: miscellaneouscatalogsmethods: data analysismethods: statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 5 , Symposium S267: Co-Evolution of Central Black Holes and Galaxies , August 2009 , pp. 147
Copyright
Copyright © International Astronomical Union 2010