High-redshift quasar selection from the CFHQSIR survey

TL;DR

This paper describes a survey using near-infrared and optical imaging to identify high-redshift quasars around z ~ 7, employing a Bayesian classification method to refine candidate selection.

Contribution

It introduces an advanced Bayesian classification technique that models quasar and star populations for improved candidate ranking in high-redshift quasar searches.

Findings

Identified 36 high-redshift quasar candidates from initial colour selection.

Developed a Bayesian method that effectively distinguishes quasars from low-mass stars.

Predicted detection of approximately 2 quasars in the surveyed area and redshift range.

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 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 $\rm{\sim 130\,deg^{2}}$ of Wide-field Infrared Camera (WIRCam) Y-band images up to a 5{\sigma} limit of $\rm{Y_{AB}}$ ~ 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 $\rm{M_{1450}}$ = -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.