Random forests method to discover high-redshift QSOs

We find that, thanks to the increasing set of known quasars, we can assemble a training set that enables supervised machine-learning algorithms to become a competitive alternative to other methods up to this redshift. We present a candidate set for the redshift range 4.8-6.3, which includes the region around z=5.5 where selecting quasars is difficult due to their photometric similarity to red and brown dwarfs.

We demonstrate that, under our survey restrictions, we can reach a high completeness (66%{+/-}7% below redshift 5.6/83_-9_^+6^% above redshift 5.6) while maintaining a high selection efficiency (78_-8_^+10^%/94_-8_^+5^% ). Our selection efficiency is estimated via a novel method based on the different distributions of quasars and contaminants on the sky. The final catalog of 515 candidates includes 225 known quasars.