Classification and photometric redshift estimation of quasars in photometric surveys. Issue Volume 15:Issue S359(2020) (March 2019) - Izuti Nakazono, L. M.; Mendes de Oliveira, C.; Hirata, N. S. T.; Jeram, S.; Gonzalez, A.; Eikenberry, S.; Queiroz, C.; Abramo, R.; Overzier, R.
Abstract: We present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.