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

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.