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

TL;DR

This paper presents a large, machine-learning-based catalog of quasar candidates from the HSC-SSP survey, demonstrating high completeness and accuracy, and providing a valuable resource for quasar and cosmological studies.

Contribution

The paper introduces the QHSC catalog with machine learning selection, multiple data samples, and ensemble photometric redshifts, enhancing quasar identification in wide-field surveys.

Findings

Over 1.18 million quasar candidates identified in the catalog.

Achieved high completeness rates up to 92.7% across samples.

Developed ensemble photometric redshift estimators with low outlier fractions.

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 $\sim 1470$ deg$^2$ to a depth of $i \sim 26$ 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.