The VIMOS Public Extragalactic Redshift Survey (VIPERS). A Support Vector Machine classification of galaxies, stars and AGNs
K. Malek, A. Solarz, A. Pollo, A. Fritz, B. Garilli, M. Scodeggio, A., Iovino, B. R. Granett, U. Abbas, C. Adami, S. Arnouts, J. Bel, M. Bolzonella,, D. Bottini, E. Branchini, A. Cappi, J. Coupon, O. Cucciati, I. Davidzon, G., De Lucia, S. de la Torre, P. Franzetti, M. Fumana

TL;DR
This paper develops a Support Vector Machine-based classification method using optical and NIR photometry to accurately identify stars, galaxies, and AGNs in large sky surveys like VIPERS, improving sample purity and classification efficiency.
Contribution
The paper introduces a tailored SVM classifier that outperforms traditional methods in classifying astronomical objects using multi-band photometry, validated on VIPERS data.
Findings
Achieved 97% accuracy in classifying galaxies and stars.
Including NIR data significantly improves classifier efficiency.
Validated the classifier on VIPERS data with over 94% accuracy for galaxies.
Abstract
The aim of this work is to develop a comprehensive method for classifying sources in large sky surveys and we apply the techniques to the VIMOS Public Extragalactic Redshift Survey (VIPERS). Using the optical (u*, g', r', i') and NIR data (z', Ks), we develop a classifier, based on broad-band photometry, for identifying stars, AGNs and galaxies improving the purity of the VIPERS sample. Support Vector Machine (SVM) supervised learning algorithms allow the automatic classification of objects into two or more classes based on a multidimensional parameter space. In this work, we tailored the SVM for classifying stars, AGNs and galaxies, and applied this classification to the VIPERS data. We train the SVM using spectroscopically confirmed sources from the VIPERS and VVDS surveys. We tested two SVM classifiers and concluded that including NIR data can significantly improve the efficiency of…
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Taxonomy
TopicsSpectroscopy and Chemometric Analyses · Galaxies: Formation, Evolution, Phenomena · Spectroscopy Techniques in Biomedical and Chemical Research
