Automated Classification of Quasars and Stars
Abstract
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.
- Publication:
-
Co-Evolution of Central Black Holes and Galaxies
- Pub Date:
- May 2010
- DOI:
- 10.1017/S1743921310006083
- Bibcode:
- 2010IAUS..267..147Z
- Keywords:
-
- astronomical databases: miscellaneous;
- catalogs;
- methods: data analysis;
- methods: statistical