Classification and photometric redshift estimation of quasars in photometric surveys. Issue Volume 15:Issue S359(2020) (March 2019)

• Record Type: Journal Article
• Authors: Izuti Nakazono, L. M.; Mendes de Oliveira, C.; Hirata, N. S. T.; Jeram, S.; Gonzalez, A.; Eikenberry, S.; Queiroz, C.; Abramo, R.; Overzier, R.
• Editors: Bergmann, Thaisa Storchi; Forman, William; Overzier, Roderik; Riffel, Rogério
• Is Part Of: Proceedings of the International Astronomical Union. Volume 15:Issue S359(2020)
• Page Start: 40
• Page End: 41
• Publication Date: 2019-03
• Subjects: methods: statistical, -- catalogs, -- quasars: general; Astronomy -- Congresses; Astronomy -- Periodicals; 520
• DOI: 10.1017/S1743921320001829
• Languages: English
• ISSNs: 1743-9213

Abstract

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.