Discovery of optically faint obscured quasars with Virtual Observatory tools

¹ ST-ECF, European Southern Observatory, Karl-Schwarzschild-Str. 2, 85748 Garching bei München, Germany e-mail: Paolo.Padovani@eso.org
² Centre de Données astronomiques de Strasbourg (UMR 7550), 11 rue de l'Université, 67000 Strasbourg, France e-mail: allen@astro.u-strasbg.fr
³ European Southern Observatory, Karl-Schwarzschild-Str. 2, 85748 Garching bei München, Germany e-mail: Piero.Rosati@eso.org
⁴ Institute of Astronomy, Madingley Road, Cambridge CB3 0HA, UK e-mail: naw@ast.cam.ac.uk

Received: 23 April 2004
Accepted: 2 June 2004

Abstract

We use Virtual Observatory (VO) tools to identify optically faint, obscured (i.e., type 2) active galactic nuclei (AGN) in the two Great Observatories Origins Deep Survey (GOODS) fields. By employing publicly available X-ray and optical data and catalogues we discover 68 type 2 AGN candidates. The X-ray powers of these sources are estimated by using a previously known correlation between X-ray luminosity and X-ray-to-optical flux ratio. Thirty-one of our candidates have high estimated powers ( erg/s) and therefore qualify as optically obscured quasars, the so-called “QSO 2”. Based on the derived X-ray powers, our candidates are likely to be at relatively high redshifts, , with the QSO 2 at . By going ~3 mag fainter than previously known type 2 AGN in the two GOODS fields we are sampling a region of redshift – power space which was previously unreachable with classical methods. Our method brings to 40 the number of QSO 2 in the GOODS fields, an improvement of a factor when compared to the only 9 such sources previously known. We derive a QSO 2 surface density down to 10^-15 erg cm^-2 s^-1 in the  keV band of 330 deg^-2, ~ of which is made up of previously known sources. This is larger than current estimates and some predictions and suggests that the surface density of QSO 2 at faint flux limits has been underestimated. This work demonstrates that VO tools are mature enough to produce cutting-edge science results by exploiting astronomical data beyond “classical” identification limits () with interoperable tools for statistical identification of sources using multiwavelength information.