An automated method for finding the most distant quasars

Upcoming surveys such as Euclid, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (Roman) will detect hundreds of high-redshift (z > 7) quasars, but distinguishing them from the billions of other sources in these catalogues represents a significant data analysis challenge. We address this problem by extending existing selection methods by using both i) Bayesian model comparison on measured fluxes and ii) a likelihood-based goodness-of-fit test on images, which are then combined using the F_beta statistic (where beta is a parameter which can be tuned to prioritise completeness). The result is an automated, reproduceable and objective high-redshift quasar selection pipeline. We test this on both simulations and real data from the cross-matched Sloan Digital Sky Survey (SDSS) and UKIRT Infrared Deep Sky Survey (UKIDSS) catalogues. On this cross-matched dataset we achieve an area under the curve (AUC) score of up to 0.81 and an F_3 score of up to 0.79; or, if the completeness is fixed to be 0.9, then we can obtain an efficiency of 0.15. This is sufficient to be applied to the Euclid, LSST and Roman data when available.