Newly discovered $zsim5$ quasars based on deep learning and Bayesian information criterion

Kavli Affiliate: Linhua Jiang

| First 5 Authors: Suhyun Shin, Myungshin Im, Yongjung Kim, Linhua Jiang,

| Summary:

We report the discovery of four quasars with $M_{1450} gtrsim -25.0$ mag at
$zsim5$ 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 Ly$alpha$ breaks at about 7000-8000 r{A},
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
CRomannum{4}$lambda 1549$ emission line. The SMBH mass and Eddington ratio of
the quasar are found to be $sim 10^8 M_{odot}$ and $sim 0.6$, respectively.
This suggests that this quasar possibly harbors a fast growing SMBH near the
Eddington limit despite its faintness ($L_{rm Bol} < 10^{46}$ 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.

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