Astronomy and Astrophysics, volume 617A, 127-127 (2018/9-1)
High-redshift quasar selection from the CFHQSIR survey.
PIPIEN S., CUBY J.-G., BASA S., WILLOTT C.J., CUILLANDRE J.-C., ARNOUTS S. and HUDELOT P.
Abstract (from CDS):
Being observed only one billion years after the Big Bang, z∼7 quasars are a unique opportunity for exploring the early Universe. However, only two z∼7 quasars have been discovered in near-infrared surveys: the quasars ULAS J1120+0641 and ULAS J1342+0928 at z=7.09 and z=7.54, respectively. The rarity of these distant objects, combined with the difficulty of distinguishing them from the much more numerous population of Galactic low-mass stars, requires using efficient selection procedures. The Canada-France High-z Quasar Survey in the Near Infrared (CFHQSIR) has been carried out to search for z∼7 quasars using near-infrared and optical imaging from the Canada-France Hawaii Telescope (CFHT). Our data consist of ∼130deg2 of Wide-field Infrared Camera (WIRCam) Y-band images up to a 5σ limit of YAB∼22.4 distributed over the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) Wide fields. After follow-up observations in J band, a first photometric selection based on simple colour criteria led us to identify 36 sources with measured high-redshift quasar colours. However, we expect to detect only ∼2 quasars in the redshift range 6.8<z<7.5 down to a rest-frame absolute magnitude of M1450=-24.6. With the motivation of ranking our high-redshift quasar candidates in the best possible way, we developed an advanced classification method based on Bayesian formalism in which we model the high-redshift quasars and low-mass star populations. The model includes the colour diversity of the two populations and the variation in space density of the low-mass stars with Galactic latitude, and it is combined with our observational data. For each candidate, we compute the probability of being a high-redshift quasar rather than a low-mass star. This results in a refined list of the most promising candidates. Our Bayesian selection procedure has proven to be a powerful technique for identifying the best candidates of any photometrically selected sample of objects, and it is easily extendable to other surveys.