Quasar candidate selection and redshift estimation using J-PLUS DR3 data

ABSTRACT A comprehensive and high-purity quasar candidate catalogue with precise redshift measurements is crucial for advancing quasar research and cosmology. In the era of extensive sky surveys, the efficient identification of quasars from large-scale data sets has become a significant challenge in modern astronomy. By cross-matching the J-PLUS DR3 data set with unWISE and numerous spectroscopic data sets with accurate classifications, we compiled a known sample of 740 562 sources, including 338 456 stars, 320 606 galaxies, and 81 500 quasars. Subsequently, we developed several classification models employing XGBoost, CatBoost, and deep learning techniques. Through optimization of feature selection and hyperparameter tuning for each model, we derived an optimal classification model. This model achieved an accuracy of 99 per cent, with the Precision and Recall for quasar detection reaching 98.20 per cent and 99.39 per cent, respectively. In parallel, we utilized the known quasar sample to train an optimal model for redshift estimation, achieving a mean squared error of 0.139. Finally, combining the optimal classification and regression models, we designed an efficient workflow for quasar candidate selection and redshift estimation. This process resulted in the identification of over 3 million quasar candidates with photometric redshifts from the J-PLUS DR3 data set. These candidates provide an invaluable input catalogue for subsequent observations by large-scale spectroscopic surveys, such as LAMOST, SDSS, DESI, or other ongoing efforts in this field.

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Publication Details

Journal
Monthly Notices of the Royal Astronomical Society
Published
2025-08-28
DOI
https://doi.org/10.1093/mnras/staf1427
Primary Topic
Gamma-ray bursts and supernovae
Type
article
Field-Weighted Citation Impact
0.00

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article

Quasar candidate selection and redshift estimation using J-PLUS DR3 data

Changhua Li, Chenzhou Cui, Ji Li, Jingyi Zhang et al.
Monthly Notices of the Royal Astronomical Society
Gamma-ray bursts and supernovae
article

Quasar candidate selection and redshift estimation using J-PLUS DR3 data

Changhua Li, Chenzhou Cui, Ji Li, Jingyi Zhang, Yanxia Zhang, Xue-Bing Wu, Xing-Yu Yang, Chao Tang, S. L. Wei
article en

Abstract

ABSTRACT A comprehensive and high-purity quasar candidate catalogue with precise redshift measurements is crucial for advancing quasar research and cosmology. In the era of extensive sky surveys, the efficient identification of quasars from large-scale data sets has become a significant challenge in modern astronomy. By cross-matching the J-PLUS DR3 data set with unWISE and numerous spectroscopic data sets with accurate classifications, we compiled a known sample of 740 562 sources, including 338 456 stars, 320 606 galaxies, and 81 500 quasars. Subsequently, we developed several classification models employing XGBoost, CatBoost, and deep learning techniques. Through optimization of feature selection and hyperparameter tuning for each model, we derived an optimal classification model. This model achieved an accuracy of 99 per cent, with the Precision and Recall for quasar detection reaching 98.20 per cent and 99.39 per cent, respectively. In parallel, we utilized the known quasar sample to train an optimal model for redshift estimation, achieving a mean squared error of 0.139. Finally, combining the optimal classification and regression models, we designed an efficient workflow for quasar candidate selection and redshift estimation. This process resulted in the identification of over 3 million quasar candidates with photometric redshifts from the J-PLUS DR3 data set. These candidates provide an invaluable input catalogue for subsequent observations by large-scale spectroscopic surveys, such as LAMOST, SDSS, DESI, or other ongoing efforts in this field.

Monthly Notices of the Royal Astronomical SocietyVol. 542(4)
Peking University (CN), Kavli Institute for Theoretical Sciences (CN), Astronomical Observatory (RS), National Astronomical Observatories (CN), University of Chinese Academy of Sciences (CN), Hebei Normal University (CN)
National Science Foundation, Smithsonian Institution, U.S. Department of Energy, National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Gordon and Betty Moore Foundation, Yale University, Carnegie Institution of Washington, Vanderbilt University, Ohio State University, California Institute of Technology, University of Utah, University of Washington, Johns Hopkins University, Carnegie Mellon University, University of Notre Dame, University of Portsmouth, New Mexico State University, Ministerio de Ciencia, Innovación y Universidades, European Space Agency, York University, University of Oxford, Fundação de Amparo à Pesquisa do Estado de São Paulo, National Natural Science Foundation of China, Chinese Academy of Sciences, Ministerio de Economía y Competitividad, Ministério da Ciência, Tecnologia e Inovação, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Ministerio de Ciencia e Innovación, Universidad Nacional Autónoma de México, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, National Development and Reform Commission, Jiangsu Association for Science and Technology, Max-Planck-Institut für Astrophysik, Max-Planck-Institut für Astronomie, Instituto de Astrofísica de Canarias, Office of Science, Jet Propulsion Laboratory, University of California, Los Angeles, University of Colorado Boulder, Smithsonian Astrophysical Observatory, Science and Technology Facilities Council, Leibniz-Gemeinschaft, Agencia Estatal de Investigación, National Key Research and Development Program of China, National Astronomical Observatories, Chinese Academy of Sciences, Division of Astronomical Sciences, Lawrence Berkeley National Laboratory
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Gamma-ray bursts and supernovae
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