A robust classification of high-redshift galaxies using support vector machines

Details A robust classification of high-redshift galaxies using support vector machines A robust classification of high-redshift galaxies using support vector machines

Jul 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007sf2a.conf..338h&link_type=abstract

SF2A-2007: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics held in Grenoble, France, July

Astronomy and Astrophysics

Astrophysics

Scientific paper

We present a new non-parametric method to quantify morphologies of galaxies based on a particular family of learning machines called support vector machines. The method, that can be seen as a generalization of the classical CAS classification but with an unlimited number of dimensions and non-linear boundaries between decision regions, is fully automated and thus particularly well adapted to large cosmological surveys. The source code is available for download at http://www.lesia.obspm.fr/˜huertas/galsvm.html. To test the method, we use a seeing limited near-infrared (Ks band, 2,16μ m) sample observed with WIRCam at CFHT at a median redshift of z˜0.8. The machine is trained with a simulated sample built from a local visually classified sample from the SDSS chosen in the high-redshift sample's rest-frame (i band, 0.77μ m ) and artificially redshifted to match the observing conditions. We use a 12-dimensional volume, including 5 morphological parameters, and other caracteristics of galaxies such as luminosity and redshift. A fraction of the simulated sample is used to test the machine and assess its accuracy. We show that a qualitative separation in two main morphological types (late type and early type) can be obtained with an error lower than 20% up to the completeness limit of the sample (KAB˜ 22) which is more than 2 times better that what would be obtained with a classical C/A classification on the same sample and indeed comparable to space data. The method is optimized to solve a specific problem, offering an objective and automated estimate of errors that enables a straightforward comparison with other surveys. Selecting the training sample in the high-redshift sample rest-frame makes the results free from wavelength dependent effects and hence its interpretation in terms of evolution easier.

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