Photometric Classification of Stars, Galaxies and Quasars in the Sloan Digital Sky Survey DR6 Using Support Vector Machines

Details Photometric Classification of Stars, Galaxies and Quasars in the Sloan Digital Sky Survey DR6 Using Support Vector Machines Photometric Classification of Stars, Galaxies and Quasars in the Sloan Digital Sky Survey DR6 Using Support Vector Machines

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008aipc.1082....9e&link_type=abstract

CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: ``Classification and Di

Computer Science

Performance

1

Binary And Multiple Stars, Quasars, Active Or Peculiar Galaxies, Objects, And Systems, Quasars

Scientific paper

This paper describes the automated classification of objects from the DR6 release of the Sloan Digital Sky Survey (SDSS) using support vector machines (SVM). First the SVM classifier was trained on a dataset comprising the u-g, g-r, r-i and i-z colours of 47,401 stars, 415,634 galaxies and 71,031 quasars with spectral classifications. An analysis of the performance of the classifier showed a total classification error of 3.80% and demonstrates that the SVM is efficiently able to learn the non-linear, four dimensional class boundaries. Afterwards class membership probabilities for stars, galaxies and quasars were predicted for 12,362,179 objects in DR6 without spectra which were situated within the inner 90% of the training colour space and had magnitude errors below 10%. The SVM predicted 11,012,775 stars, 1,088,862 galaxies and 260,542 quasars. The relatively high number of galaxies can be explained by our constraints on colours and magnitude errors. The results were validated by cross-matching against the FIRST and USNO-B surveys. The cross-match with FIRST resulted in 8,666 radio sources of which the SVM classifier predicted 94.8% to be galaxies or quasars as expected.

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