CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE

Abstract A complete and pure sample of quasars with accurate redshifts is crucial for quasar studies and cosmology. In this paper, we present CatNorth, an improved Gaia Data Release 3 (Gaia DR3) quasar candidate catalog with more than 1.5 million sources in the 3 π sky built with data from Gaia, Pan...

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Bibliographic Details
Published in:The Astrophysical journal. Supplement series 2024-04, Vol.271 (2), p.54
Main Authors: Fu, Yuming, Wu, Xue-Bing, Li, Yifan, Pang, Yuxuan, Joshi, Ravi, Zhang, Shuo, Wang, Qiyue, Yang, Jing, Ng, FanLam, Liu, Xingjian, Qiu, Yu, Zhu, Rui, Wang, Huimei, Wolf, Christian, Zhang, Yanxia, Huo, Zhi-Ying, Ai, Y. L., Ma, Qinchun, Feng, Xiaotong, Bouwens, R. J.
Format: Article
Language:English
Subjects:
Active galactic nuclei
Algorithms
Artificial neural networks
Astrostatistics techniques
Candidates
Catalogues
Classification
Cosmology
Galaxies
Neural networks
Probability theory
Quasars
Redshift surveys
Regression models
Sky surveys (astronomy)
Spectroscopic telescopes
Spectroscopy
Statistical analysis
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Summary:Abstract A complete and pure sample of quasars with accurate redshifts is crucial for quasar studies and cosmology. In this paper, we present CatNorth, an improved Gaia Data Release 3 (Gaia DR3) quasar candidate catalog with more than 1.5 million sources in the 3 π sky built with data from Gaia, Pan-STARRS1, and CatWISE2020. The XGBoost algorithm is used to reclassify the original Gaia DR3 quasar candidates as stars, galaxies, and quasars. To construct training/validation data sets for the classification, we carefully built two different master stellar samples in addition to the spectroscopic galaxy and quasar samples. An ensemble classification model is obtained by averaging two XGBoost classifiers trained with different master stellar samples. Using a probability threshold of p QSO_mean > 0.95 in our ensemble classification model and an additional cut on the logarithmic probability density of zero proper motion, we retrieved 1,545,514 reliable quasar candidates from the parent Gaia DR3 quasar candidate catalog. We provide photometric redshifts for all candidates with an ensemble regression model. For a subset of 89,100 candidates, accurate spectroscopic redshifts are estimated with the convolutional neural network from the Gaia BP/RP spectra. The CatNorth catalog has a high purity of ∼90%, while maintaining high completeness, which is an ideal sample to understand the quasar population and its statistical properties. The CatNorth catalog is used as the main source of input catalog for the Large Sky Area Multi-Object Fiber Spectroscopic Telescope phase III quasar survey, which is expected to build a highly complete sample of bright quasars with i < 19.5.
ISSN:0067-0049
1538-4365
DOI:10.3847/1538-4365/ad2ae6