Use of neural networks for the identification of new z≥ 3.6 QSOs from FIRST–SDSS DR5.

We aim to obtain a complete sample of redshift radio quasi-stellar objects (QSOs) from the Faint Images of the Radio Sky at Twenty cm survey (FIRST) sources having star-like counterparts in the Sloan Digital Sky Survey (SDSS) Data Release 5 (DR5) photometric survey . Our starting sample of 8665 FIRST–DR5 pairs includes 4250 objects with spectra in DR5, 52 of these being 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 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 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 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 QSOs of 15 per cent for . [ABSTRACT FROM AUTHOR]