QHSC: The Quasar Candidate Catalog for the Hyper Suprime-Cam Subaru Strategic Program

2025/11/18 by Zhu, Rui, Wu, Xue-Bing, Pang, Yuxuan +1
Physics and Astronomy · #Astronomy and Astrophysical Research #Astrophysics of Galaxies (astro-ph.GA) #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Radio Astronomy Observations and Technology

paper · doi:10.48550/arxiv.2511.14369

openalex publication_date 2025/11/18 · openalex created_date 2025/11/20 · openalex updated_date 2026/07/28

Abstract

The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) is a deep wide-field multi-band imaging survey consisting of three layers (Wide, Deep, and UltraDeep), with the Wide layer covering ∼ 1470 deg² to a depth of i ∼ 26 mag. We present the QHSC catalog, a machine-learning selected sample of quasar candidates with photometric redshifts in the Wide layer of the HSC-SSP survey (Public Data Release 3). The full QHSC catalog contains four distinct samples: a master sample with HSC-only photometry, an HSC+WISE sample, and two samples including near-infrared data from UKIDSS and VISTA, denoted as GoldenU and GoldenV. For each sample, an XGBoost classifier is trained and evaluated using independent spectroscopic test sets from HETDEX, VVDS, and zCOSMOS-bright. The numbers of quasar candidates in the QHSC catalog are 1,184,574 (master), 371,777 (HSC+WISE), 87,460 (GoldenU), and 120,572 (GoldenV), with respective completeness values of 85.3%, 92.7%, 89.8%, and 91.3%. We develop ensemble photometric redshift estimators based on bootstrap aggregating (bagging) of multiple XGBoost regressors, achieving outlier fractions of 21.7%, 13.1%, 9.5%, and 10.7% for these samples. The catalog provides quasar classification probabilities (p_QSO), enabling construction of purer subsamples via thresholding. This work offers a valuable resource for studies of quasars and cosmology, and highlights the effectiveness of machine learning for quasar selection in future wide and deep imaging surveys. The catalog is publicly available at https://doi.org/10.5281/zenodo.17515028.

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