Euclidpreparation

Abstract

We provide predictions of the yield of 7 < z < 9 quasars from the Euclid wide survey, updating the calculation presented in the Euclid Red Book in several ways. We account for revisions to the Euclid near-infrared filter wavelengths; we adopt steeper rates of decline of the quasar luminosity function (QLF; Φ) with redshift, Φ ∝ 10 k(z−6) , k = −0.72, and a further steeper rate of decline, k = −0.92; we use better models of the contaminating populations (MLT dwarfs and compact early-type galaxies); and we make use of an improved Bayesian selection method, compared to the colour cuts used for the Red Book calculation, allowing the identification of fainter quasars, down to J AB ∼ 23. Quasars at z > 8 may be selected from Euclid OY JH photometry alone, but selection over the redshift interval 7 < z < 8 is greatly improved by the addition of z-band data from, e.g., Pan-STARRS and LSST. We calculate predicted quasar yields for the assumed values of the rate of decline of the QLF beyond z = 6. If the decline of the QLF accelerates beyond z = 6, with k = −0.92, Euclid should nevertheless find over 100 quasars with 7.0 < z < 7.5, and ∼25 quasars beyond the current record of z = 7.5, including ∼8 beyond z = 8.0. The first Euclid quasars at z > 7.5 should be found in the DR1 data release, expected in 2024. It will be possible to determine the bright-end slope of the QLF, 7 < z < 8, M 1450 < −25, using 8 m class telescopes to confirm candidates, but follow-up with JWST or E-ELT will be required to measure the faint-end slope. Contamination of the candidate lists is predicted to be modest even at J AB ∼ 23. The precision with which k can be determined over 7 < z < 8 depends on the value of k, but assuming k = −0.72 it can be measured to a 1σ uncertainty of 0.07.

A complete search for redshift z ≳ 6.5 quasars in the VIKING survey

“…Although we do not use the candidate lists, for the sake of comparison of the efficiency and depth of the methods we also produce candidate lists using the SED fitting and colour cuts methods. This extends the comparison of methods undertaken by Euclid Collaboration: Barnett et al (2019), and has the advantage of using real data.…”

“…We use an extension of the BMC technique proposed by Mortlock et al (2012). The extended method is additionally described in detail by Euclid Collaboration: Barnett et al (2019), so we only recap the main points here.…”

“…The BMC method additionally requires models of the surface density, as a function of apparent magnitude, of the contaminating populations and of the quasars. The populations modelled are the same as we used in the study (Euclid Collaboration: Barnett et al 2019), but adapted specifically for VIKING data, taking into consideration the different filter transmission curves and image quality. The full details of the of the models of the populations (colours and surface densities of quasars and contaminants) are provided in the Appendix, Sect.…”

“…The main purpose of the current paper is to use the VISTA Kilo-Degree Infrared Galaxy (VIKING) survey (Edge et al 2013) to produce a complete sample of > 6.5 quasars (henceforth HZQs; highredshift quasars), with a computed selection function, that reaches fainter absolute magnitudes and higher redshifts than the survey of Wang et al (2019). Improving and extending measurements of the quasar luminosity function (QLF) to fainter luminosities and higher redshifts is an important aim, as such studies will constrain models of the formation and growth of supermassive black holes at early times (e.g., Willott et al 2010;Jiang et al 2016), and are also important for designing future surveys for quasars at ∼ 7 and beyond, such as for Euclid (Euclid Collaboration: Barnett et al 2019). The VIKING survey has already been searched using colour cuts, yielding four HZQs (Venemans et al 2013;Wang et al 2017).…”

“…The same method has been adapted by Pipien et al (2018) and applied to CFHT data. In a previous paper (Euclid Collaboration: Barnett et al 2019) we compared two different search methods, namely BMC and minimum-2 SED fitting (e.g., Reed et al 2017, applied to DES data). Using simulated datasets we showed that the BMC method is the more useful method as it reaches much deeper than the SED fitting method, while still being highly efficient.…”