Quasar candidates selection in the Virtual Observatory era

TL;DR

This paper introduces a novel photometric method using unsupervised clustering algorithms to select quasar candidates from multiband survey data, achieving high efficiency and completeness.

Contribution

It applies Probabilistic Principal Surfaces and Negative Entropy clustering in an astronomical context, improving quasar candidate selection in the Virtual Observatory era.

Findings

High efficiency and completeness in quasar candidate selection.

Method performs well on optical and infrared data.

Publicly available catalog of quasar candidates from SDSS DR7.

Abstract

We present a method for the photometric selection of candidate quasars in multiband surveys. The method makes use of a priori knowledge derived from a subsample of spectroscopic confirmed QSOs to map the parameter space. The disentanglement of QSOs candidates and stars is performed in the colour space through the combined use of two algorithms, the Probabilistic Principal Surfaces and the Negative Entropy clustering, which are for the first time used in an astronomical context. Both methods have been implemented in the VONeural package on the Astrogrid VO platform. Even though they belong to the class of the unsupervised clustering tools, the performances of the method are optimized by using the available sample of confirmed quasars and it is therefore possible to learn from any improvement in the available "base of knowledge". The method has been applied and tested on both optical and optical plus near infrared data extracted from the visible SDSS and infrared UKIDSS-LAS public databases. In all cases, the experiments lead to high values of both efficiency and completeness, comparable if not better than the methods already known in the literature. A catalogue of optical candidate QSOs extracted from the SDSS DR7 Legacy photometric dataset has been produced and is publicly available at the URL voneural.na.infn.it/qso.html.