@article{10902/27447,
year = {2008},
url = {https://hdl.handle.net/10902/27447},
abstract = {We aim to obtain a complete sample of redshift z ? 3.6 radio quasi-stellar objects (QSOs) from the Faint Images of the Radio Sky at Twenty cm survey (FIRST) sources (S1.4 GHz > 1 mJy) having star-like counterparts in the Sloan Digital Sky Survey (SDSS) Data Release 5 (DR5) photometric survey (rAB ? 20.2). Our starting sample of 8665 FIRST?DR5 pairs includes 4250 objects with spectra in DR5, 52 of these being z ? 3.6 QSOs. We found that simple supervised neural networks, trained on the sources with DR5 spectra, and using optical photometry and radio data, are very effective for identifying high-z QSOs in a sample without spectra. For the sources with DR5 spectra the technique yields a completeness (fraction of actual high-z QSOs classified as such by the neural network) of 96 per cent, and an efficiency (fraction of objects selected by the neural network as high-z QSOs that actually are high-z QSOs) of 62 per cent. Applying the trained networks to the 4415 sources without DR5 spectra we found 58 z ? 3.6 QSO candidates. We obtained spectra of 27 of them, and 17 are confirmed as high-z QSOs. Spectra of 13 additional candidates from the literature and from SDSS Data Release 6 (DR6) revealed seven more z ? 3.6 QSOs, giving an overall efficiency of 60 per cent (24/40). None of the non-candidates with spectra from NASA/IPAC Extragalactic Database (NED) or DR6 is a z ? 3.6 QSO, consistently with a high completeness. The initial sample of high-z QSOs is increased from 52 to 76 sources, i.e. by a factor of 1.46. From the new identifications and candidates we estimate an incompleteness of SDSS for the spectroscopic classification of FIRST 3.6 ? z ? 4.6 QSOs of 15 per cent for r ? 20.2.},
organization = {ACKNOWLEDGMENTS: We are grateful to the referee for a prompt and useful report, which improved the paper. RC, JIG-S, CRB and FJ-L acknowledge financial support from the Spanish Ministerio de Educacion y Ciencia ´ under project AYA 2005-00055. We acknowledge the Isaac Newton Group’s (ING) service programme for a generous allocation of observing time for this project and we thank ING staff for carrying out the observations. Funding for the creation and distribution of the SDSS Archive has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Aeronautics and Space Administration, the National Science Foundation, the US Department of Energy, the Japanese Monbukagakusho and the Max Planck Society. The SDSS Web site is http://www.sdss.org/. The SDSS is managed by the Astrophysical Research Consortium (ARC) for the Participating Institutions. The Participating Institutions are the University of Chicago, Fermilab, the Institute for Advanced Study, the Japan Participation Group, the Johns Hopkins University, the Korean Scientist Group, Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, University of Pittsburgh, University of Portsmouth, Princeton University, the United States Naval Observatory and the University of Washington. This research has made use of the NED which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.},
publisher = {Oxford University Press},
publisher = {Monthly Notices of the Royal Astronomical Society 2008, 391 (1), 369 - 382},
title = {Use of neural networks for the identification of new z ≥ 3.6 QSOs from FIRST-SDSS DR5},
author = {Carballo Fidalgo, Ruth and González Serrano, José Ignacio and Benn, C.R. and Jimenez Lujan, Florencia},
}