Classification of AGNs from stars and normal galaxies by support vector machines

Details Classification of AGNs from stars and normal galaxies by support vector machines Classification of AGNs from stars and normal galaxies by support vector machines

Dec 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002spie.4847..371z&link_type=abstract

Astronomical Data Analysis II. Edited by Starck, Jean-Luc; Murtagh, Fionn D. Proceedings of the SPIE, Volume 4847, pp. 371-3

Astronomy and Astrophysics

Astronomy

3

Scientific paper

In order to explore the spectral energy distribution of various objects in a multidimensional parameter space, the multiwavelenghth data of quasars, BL Lacs, active galaxies, stars and normal galaxies are obtained by positional cross-identification, which are from optical(USNO A-2), X-ray(ROSAT), infrared(2MASS) bands. Different classes of X-ray emitters populate distinct regions of a multidimensional parameter space. In this paper, an automatic classification technique called Support Vector Machines(SVMs) is put forward to classify them using 7 parameters and 10 parameters. Finally the results show SVMs is an effective method to separate AGNs from stars and normal galaxies with data from optical, X-ray bands and with data from optical, X-ray, infrared bands. Furthermore, we conclude that to classify objects is influenced not only by the method, but also by the chosen wavelengths. Moreover it is evident that the more wavelengths we choose, the higher the accuracy is.

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