Aims.The aim of this work is to develop a comprehensive method for classifying sources in large sky surveys and to apply the techniques to theVIMOS Public Extragalactic Redshift Survey (VIPERS). Using the optical (u∗, g’, r’, i’) and NIR data (z’, Ks), we develop a classifier, based onbroad-band photometry, for identifying stars, AGNs, and galaxies, thereby improving the purity of the VIPERS sample.Methods.Support vector machine (SVM) supervised learning algorithms allow the automatic classification of objects into two or more classesbased on a multidimensional parameter space. In this work, we tailored the SVM to classifying stars, AGNs, and galaxies and applied thisclassification to the VIPERS data. We trained the SVM using spectroscopically confirmed sources from the VIPERS and VVDS surveys.Results.We tested two SVM classifiers and conclude that including NIRdata can significantly improve the efficiency of the classifier. Theself-check of the best optical+NIR classifier has shown 97% accuracy in the classification ofgalaxies, 97% for stars, and 95% for AGNs in the5-dimensional colour space. In the test of VIPERS sources with 99% redshift confidence, the classifier gives an accuracy equal to 94% for galaxies,93% for stars, and 82% for AGNs. The method was applied to sources with low-quality spectra to verify their classification, hence increasing thesecurity of measurements for almost 4 900 objects.Conclusions.We conclude that the SVM algorithm trained on a carefully selected sample of galaxies, AGNs, and stars outperforms simplecolour-colour selection methods, and can be regarded as a very efficient classification method particularly suitable for modern large surveys.

The VIMOS Public Extragalactic Redshift Survey (VIPERS). A support vector machine classification of galaxies, stars, and AGNs

Branchini E;
2013

Abstract

Aims.The aim of this work is to develop a comprehensive method for classifying sources in large sky surveys and to apply the techniques to theVIMOS Public Extragalactic Redshift Survey (VIPERS). Using the optical (u∗, g’, r’, i’) and NIR data (z’, Ks), we develop a classifier, based onbroad-band photometry, for identifying stars, AGNs, and galaxies, thereby improving the purity of the VIPERS sample.Methods.Support vector machine (SVM) supervised learning algorithms allow the automatic classification of objects into two or more classesbased on a multidimensional parameter space. In this work, we tailored the SVM to classifying stars, AGNs, and galaxies and applied thisclassification to the VIPERS data. We trained the SVM using spectroscopically confirmed sources from the VIPERS and VVDS surveys.Results.We tested two SVM classifiers and conclude that including NIRdata can significantly improve the efficiency of the classifier. Theself-check of the best optical+NIR classifier has shown 97% accuracy in the classification ofgalaxies, 97% for stars, and 95% for AGNs in the5-dimensional colour space. In the test of VIPERS sources with 99% redshift confidence, the classifier gives an accuracy equal to 94% for galaxies,93% for stars, and 82% for AGNs. The method was applied to sources with low-quality spectra to verify their classification, hence increasing thesecurity of measurements for almost 4 900 objects.Conclusions.We conclude that the SVM algorithm trained on a carefully selected sample of galaxies, AGNs, and stars outperforms simplecolour-colour selection methods, and can be regarded as a very efficient classification method particularly suitable for modern large surveys.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1071354
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