Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-24T15:18:50.892Z Has data issue: false hasContentIssue false

Classification and photometric redshift estimation of quasars in photometric surveys

Published online by Cambridge University Press:  29 March 2021

L. M. Izuti Nakazono
Affiliation:
Instituto de Astronomia, Geofísica e Ciências Atmosféricas da U. de São Paulo, Cidade Universitária, 05508-900, São Paulo, SP, Brazil
C. Mendes de Oliveira
Affiliation:
Instituto de Astronomia, Geofísica e Ciências Atmosféricas da U. de São Paulo, Cidade Universitária, 05508-900, São Paulo, SP, Brazil
N. S. T. Hirata
Affiliation:
Departamento de Ciência da Computação, Instituto de Matemática e Estatística da USP, Cidade Universitária, 05508-090, São Paulo, SP, Brazil
S. Jeram
Affiliation:
Department of Astronomy, University of Florida, 211 Bryant Space Center, Gainesville, FL 32611, USA
A. Gonzalez
Affiliation:
Department of Astronomy, University of Florida, 211 Bryant Space Center, Gainesville, FL 32611, USA
S. Eikenberry
Affiliation:
Department of Astronomy, University of Florida, 211 Bryant Space Center, Gainesville, FL 32611, USA
C. Queiroz
Affiliation:
Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, SP, Rua do Matão 1371, São Paulo, Brazil
R. Abramo
Affiliation:
Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, SP, Rua do Matão 1371, São Paulo, Brazil
R. Overzier
Affiliation:
Observatório Nacional/MCTIC, Rua General José Cristino 77, Rio de Janeiro, RJ, 20921-400, Brazil
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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.

Type
Contributed Papers
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of International Astronomical Union

References

Moore, J. A., Pimbblet, K. A., & Drinkwater, M. J. 2006, PASA, 23, 135 CrossRefGoogle Scholar
Pâris, I., Petitjean, P. Aubourg, É., et al. 2018, A&A, 613, A51 Google Scholar
Heintz, K. E., Fynbo, J. P. U., Høg, E., et al. 2018, A&A, 615, L8 Google Scholar
Yang, Q., Wu, X., Fan, X., et al. 2017, AJ, 154, 269 CrossRefGoogle Scholar
Jin, X., Zhang, Y., Zhang, J., et al. 2019, MNRAS, 485, 4539 CrossRefGoogle Scholar
Mendes de Oliveira, C., Ribeiro, T., Schoenell, W., et al. 2019, MNRAS, 489, 241 CrossRefGoogle Scholar