Buscar

Estamos realizando la búsqueda. Por favor, espere...

Neural-network selection of high-redshift radio quasars, and the luminosity function at z ~ 4

Abstract: We obtain a sample of 87 radio-loud quasi-stellar objects (QSOs) in the redshift range 3.6 = z = 4.4 by cross-correlating sources in the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) radio survey (S1.4?GHz > 1 mJy) with star-like objects having r < 20.2 in Sloan Digital Sky Survey (SDSS) Data Release 7. Of these 87 QSOs, 80 are spectroscopically classified in previous work (mainly SDSS), and form the training set for a search for additional such sources. We apply our selection to 2916 FIRST-DR7 pairs and find 15 likely candidates. Seven of these are confirmed as high-redshift quasars, bringing the total to 87. The candidates were selected using a neural-network, which yields 97?per?cent completeness (fraction of actual high-z QSOs selected as such) and an efficiency (fraction of candidates which are high-z QSOs) in the range of 47–60?per?cent. We use this sample to estimate the binned optical luminosity function (LF) of radio-loud QSOs at z ~ 4, and also the LF of the total QSO population and its comoving density. Our results suggest that the radio-loud fraction at high z is similar to that at low z and that other authors may be underestimating the fraction at high z. Finally, we determine the slope of the optical LF and obtain results consistent with previous studies of radio-loud QSOs and of the whole population of QSOs. The evolution of the LF with redshift was for many years interpreted as a flattening of the bright-end slope, but has recently been re-interpreted as strong evolution of the break luminosity for high-z QSOs, and our results, for the radio-loud population, are consistent with this.

 Fuente: Monthly Notices of the Royal Astronomical Society, vol. 449, iss.3, Pp. 2818-2836 (2015)

 Editorial: Oxford University Press

 Fecha de publicación: 01/05/2015

 Nº de páginas: 19

 Tipo de publicación: Artículo de Revista

 DOI: 10.1093/mnras/stv472

 ISSN: 0035-8711,1365-2966

 Proyecto español: AYA2011-29517-C03-02

 Url de la publicación: https://doi.org/10.1093/mnras/stv472