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

Abstract

We present the results of a new, deeper, and complete search for high-redshift $6.5<z<9.3$ quasars over 977deg${}^2$ of the VISTA Kilo-Degree Infrared Galaxy (VIKING) survey. This exploits a new list-driven dataset providing photometry in all bands ZYJHKs, for all sources detected by VIKING in $J$. We use the Bayesian model comparison (BMC) selection method of Mortlock et al., producing a ranked list of just 21 candidates. The sources ranked 1, 2, 3 and 5 are the four known $z>6.5$ quasars in this field. Additional observations of the other 17 candidates, primarily DESI Legacy Survey photometry and ESO FORS2 spectroscopy, confirm that none is a quasar. This is the first complete sample from the VIKING survey, and we provide the computed selection function. We include a detailed comparison of the BMC method against two other selection methods: colour cuts and minimum-${\chi }^2$ SED fitting. We find that: i) BMC produces eight times fewer false positives than colour cuts, while also reaching 0.3 mag. deeper, ii) the minimum-${\chi }^2$ SED fitting method is extremely efficient but reaches 0.7 mag. less deep than the BMC method, and selects only one of the four known quasars. We show that BMC candidates, rejected because their photometric SEDs have high ${\chi }^2$ values, include bright examples of galaxies with very strong [OIII]$\lambda \lambda$4959,5007 emission in the $Y$ band, identified in fainter surveys by Matsuoka et al. This is a potential contaminant population in Euclid searches for faint $z>7$ quasars, not previously accounted for, and that requires better characterisation.

Accepted for MNRAS

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Cited by in corpus (2)

• Paving the Way for Euclid and JWST via Optimal Selection of High-z Quasars
• QUEST (Quasar Unsupervised Encoder and Synthesis Tool): A machine learning framework to generate quasar spectra