Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 509, Issue 2, Pages 2289-2303Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stab3165
Keywords
methods: statistical; techniques: photometric; astronomical data bases: miscellaneous; galaxies: distances and redshifts; quasars: general; galaxies: photometric
Categories
Funding
- National Natural Science Foundation of China (NSFC) [11573019, 11803055, 11873066, 12133001, 11433005]
- NSFC [U1531246, U1731125, U1731243, U1731109]
- Chinese Academy of Sciences (CAS) [U1531246, U1731125, U1731243, U1731109]
- 13th Five-year Informatization Plan of Chinese Academy of Sciences [XXH13503-03-107]
- China Manned Space Project [CMS-CSST-2021-A06]
- National Astronomical Observatories of China
- Chinese Academy of Sciences [XDB09000000]
- Special Fund for Astronomy from the Ministry of Finance
- External Cooperation Program of the Chinese Academy of Sciences [114A11KYSB20160057]
- National Development and Reform Commission
- National Aeronautics and Space Administration
- Alfred P. Sloan Foundation
- U.S. Department of Energy Office of Science
- Brazilian Participation Group
- Carnegie Institution for Science
- Carnegie Mellon University
- Chilean Participation Group
- French Participation Group
- Harvard-Smithsonian Center for Astrophysics
- Instituto de Astrofisica de Canarias
- Johns Hopkins University
- Kavli Institute for the Physics and Mathematics of the Universe IPMU)/University of Tokyo
- Lawrence Berkeley National Laboratory
- Leibniz Institut fur Astrophysik Potsdam (AIP)
- Max-PlanckInstitut fur Astronomie (MPIA Heidelberg)
- Max-Planck-Institut fur Astrophysik (MPA Garching)
- Max-Planck-Institut fur Extraterrestrische Physik (MPE)
- New Mexico State University
- New York University
- University of Notre Dame
- Observatario Nacional/MCTI
- Ohio State University
- Pennsylvania State University
- Shanghai Astronomical Observatory
- United Kingdom Participation Group
- Universidad Nacional Autonoma de Mexico
- University of Arizona
- University of Colorado Boulder
- University of Oxford
- University of Portsmouth
- University of Utah
- University of Virginia
- University of Washington
- University of Wisconsin
- Vanderbilt University
- Yale University
- Fundamental Science Data Sharing Platform [DKA2017-12-02-07]
- Center for High-Performance Computing at the University of Utah
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In this study, the performance of XGBoost, CatBoost, and Random Forest algorithms in estimating photometric redshifts of quasars was compared, with CatBoost showing the best performance, especially for high-redshift quasars. The two-step model, which utilizes CatBoost as the core algorithm, outperforms the one-step model and provides more accurate redshift predictions for high-redshift quasars.
Correlating Beijing-Arizona Sky Survey (BASS) data release 3 (DR3) catalogue with the ALLWISE data base, the data from optical and infrared information are obtained. The quasars from Sloan Digital Sky Survey are taken as training and test samples while those from LAMOST are considered as external test sample. We propose two schemes to construct the redshift estimation models with XGBoost, CatBoost, and Random Forest. One scheme (namely one-step model) is to predict photometric redshifts directly based on the optimal models created by these three algorithms; the other scheme (namely two-step model) is to first classify the data into low- and high-redshift data sets, and then predict photometric redshifts of these two data sets separately. For one-step model, the performance of these three algorithms on photometric redshift estimation is compared with different training samples, and CatBoost is superior to XGBoost and Random Forest. For two-step model, the performances of these three algorithms on the classification of low and high redshift subsamples are compared, and CatBoost still shows the best performance. Therefore, CatBoost is regarded as the core algorithm of classification and regression in two-step model. In contrast to one-step model, two-step model is optimal when predicting photometric redshift of quasars, especially for high-redshift quasars. Finally, the two models are applied to predict photometric redshifts of all quasar candidates of BASS DR3. The number of high-redshift quasar candidates is 3938 (redshift >= 3.5) and 121 (redshift >= 4.5) by two-step model. The predicted result will be helpful for quasar research and follow-up observation of high-redshift quasars.
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