Newly discovered $z\sim5$ quasars based on deep learning and Bayesian information criterion

TL;DR

This paper reports the discovery of four high-redshift quasars at z~5 using deep learning and Bayesian information criterion, demonstrating an effective method for identifying faint quasars and measuring their supermassive black hole properties.

Contribution

The study introduces a novel combination of deep learning and Bayesian information criterion for efficient high-redshift quasar candidate selection, confirmed by spectroscopic observations.

Findings

Discovered four quasars at z~5 with M_{1450} > -25.0 mag.

Measured SMBH mass of one quasar as ~10^8 M_sun.

Achieved 100% identification rate validating the method.

Abstract

We report the discovery of four quasars with $M_{1450} \gtrsim -25.0$ mag at $z\sim5$ 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 C\Romannum{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.