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We present a novel method to identify candidate high redshift quasars (HzQs;($z\gtrsim5.5$), which are unique probes of supermassive black hole growth inthe early Universe, from large area optical/infrared photometric surveys. UsingGaussian Mixture Models to construct likelihoods and incorporate informedpriors based on population statistics, our method uses a Bayesian framework toassign posterior probabilities that differentiate between HzQs andcontaminating sources. We additionally include deep radio data to obtaininformed priors. Using existing HzQ data in the literature, we set a posteriorthreshold that accepts ${\sim}90\%$ of known HzQs while rejecting $>99\%$ ofcontaminants such as dwarf stars or lower redshift galaxies. Running theprobability selection on test samples of simulated HzQs and contaminants, wefind that the efficacy of the probability method is higher than traditionalcolour cuts, decreasing the fraction of accepted contaminants by 86% whileretaining a similar fraction of HzQs. As a test, we apply our method to thePan-STARRS Data Release 1 (PS1) source catalogue within the HETDEX Spring fieldarea on the sky, covering 400 sq. deg. and coinciding with deep radio data fromthe LOFAR Two-metre Sky Survey Data Release 1 (LoTSS DR1). From an initialsample of ${\sim}5\times10^5$ sources in PS1, our selection shortlists 251candidate HzQs, which are further reduced to 63 after visual inspection.Shallow spectroscopic follow-up of 13 high probability HzQs resulted in theconfirmation of a previously undiscovered quasar at $z=5.66$ with photometriccolours $i-z = 1.4$, lying outside the typically probed regions when selectingHzQs based on colours. This discovery demonstrates the efficacy of ourprobabilistic HzQ selection method in selecting more complete HzQ samples,which holds promise when employed on large existing and upcoming photometricdata sets.

Revealing new high redshift quasar populations through Gaussian mixture model selection