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The aim of this work is to develop a comprehensive method for classifyingsources in large sky surveys and we apply the techniques to the VIMOS PublicExtragalactic Redshift Survey (VIPERS). Using the optical (u*, g', r', i') andNIR data (z', Ks), we develop a classifier, based on broad-band photometry, foridentifying stars, AGNs and galaxies improving the purity of the VIPERS sample.Support Vector Machine (SVM) supervised learning algorithms allow the automaticclassification of objects into two or more classes based on a multidimensionalparameter space. In this work, we tailored the SVM for classifying stars, AGNsand galaxies, and applied this classification to the VIPERS data. We train theSVM using spectroscopically confirmed sources from the VIPERS and VVDS surveys.We tested two SVM classifiers and concluded that including NIR data cansignificantly improve the efficiency of the classifier. The self-check of thebest optical + NIR classifier has shown a 97% accuracy in the classification ofgalaxies, 97 for stars, and 95 for AGNs in the 5-dimensional colour space. Inthe test on VIPERS sources with 99% redshift confidence, the classifier givesan accuracy equal to 94% for galaxies, 93% for stars, and 82% for AGNs. Themethod was applied to sources with low quality spectra to verify theirclassification, and thus increasing the security of measurements for almost 4900 objects. We conclude that the SVM algorithm trained on a carefully selectedsample of galaxies, AGNs, and stars outperforms simple colour-colour selectionmethods, and can be regarded as a very efficient classification methodparticularly suitable for modern large surveys.

The VIMOS Public Extragalactic Redshift Survey (VIPERS). A Support Vector Machine classification of galaxies, stars and AGNs