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Classification and photometric redshift estimation of quasars in photometric surveys
Published online by Cambridge University Press: 29 March 2021
Abstract
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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.
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- © The Author(s), 2021. Published by Cambridge University Press on behalf of International Astronomical Union
References
Mendes de Oliveira, C., Ribeiro, T., Schoenell, W., et al. 2019, MNRAS, 489, 241
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