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http://dx.doi.org/10.5303/JKAS.2022.55.4.131

NEWLY DISCOVERED z ~ 5 QUASARS BASED ON DEEP LEARNING AND BAYESIAN INFORMATION CRITERION  

Shin, Suhyun (SNU Astronomy Research Center, Astronomy Program, Dept. of Physics & Astronomy, Seoul National University)
Im, Myungshin (SNU Astronomy Research Center, Astronomy Program, Dept. of Physics & Astronomy, Seoul National University)
Kim, Yongjung (Department of Astronomy and Atmospheric Sciences, College of Natural Sciences, Kyungpook National University)
Jiang, Linhua (Kavli Institute for Astronomy and Astrophysics, Peking University)
Publication Information
Journal of The Korean Astronomical Society / v.55, no.4, 2022 , pp. 131-138 More about this Journal
Abstract
We report the discovery of four quasars with M1450 ≳ -25.0 mag at z ~ 5 and supermassive black hole mass measurement for one of the quasars. They were selected as promising high-redshift quasar candidates via deep learning and Bayesian information criterion, which are expected to be effective in discriminating quasars from the late-type stars and high-redshift galaxies. The candidates were observed by the Double Spectrograph on the Palomar 200-inch Hale Telescope. They show clear Lyα breaks at about 7000-8000 Å, indicating they are quasars at 4.7 < z < 5.6. For HSC J233107-001014, we measure the mass of its supermassive black hole (SMBH) using its C IV λ1549 emission line. The SMBH mass and Eddington ratio of the quasar are found to be ~108 M⊙ and ~0.6, respectively. This suggests that this quasar possibly harbors a fast growing SMBH near the Eddington limit despite its faintness (LBol < 1046 erg s-1). Our 100% quasar identification rate supports high efficiency of our deep learning and Bayesian information criterion selection method, which can be applied to future surveys to increase high-redshift quasar sample.
Keywords
galaxies: active; galaxies: quasars: general; methods: data analysis; methods: observational; techniques: spectroscopic;
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