High-redshift quasar selection from the CFHQSIR survey

¹ Aix-Marseille Université CNRS, CNES, LAM, Marseille, France
e-mail: sarah.pipien@lam.fr
² NRC Herzberg, 5071 West Saanich Rd, Victoria, BC V9E 2E7, Canada
³ CEA/IRFU/SAp, Laboratoire AIM Paris-Saclay, CNRS/INSU, Université Paris Diderot, Observatoire de Paris, PSL Research University, 91191 Gif-sur-Yvette Cedex, France
⁴ Institut d’Astrophysique de Paris, 98bis Boulevard Arago, 75014 Paris, France

Received: 24 May 2018
Accepted: 25 August 2018

Abstract

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 ∼130 deg² of Wide-field Infrared Camera (WIRCam) Y-band images up to a 5σ limit of Y_AB ∼ 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.