Classification and photometric redshift estimation of quasars in photometric surveys

Autor:	Nina S. T. Hirata, Stephen S. Eikenberry, L. M. Izuti Nakazono, Sarik Jeram, Roderik Overzier, Conceição Queiroz, Anthony H. Gonzalez, R. Abramo, C. Mendes de Oliveira
Rok vydání:	2021
Předmět:	Methods statistical Stars QUASARES Space and Planetary Science Astronomy and Astrophysics Quasar Astrophysics::Cosmology and Extragalactic Astrophysics Astrophysics Astrophysics::Galaxy Astrophysics Galaxy Photometric redshift Mathematics Random forest
Zdroj:	Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::50bc1d956499ebed1c6763fb7713b85f Zobrazit plný text záznamu