The Quasar Luminosity Function at z 5 via Deep Learning and Bayesian Information Criterion

Abstract

Understanding the faint end of quasar luminosity function (LF) at a high redshift is important since the number density of faint quasars is a critical element in constraining ultraviolet (UV) photon budgets for ionizing the intergalactic medium (IGM) in the early universe. Here, we present quasar LF reaching M ₁₄₅₀ ~ -22.0 AB mag at z ~ 5, about 1 mag deeper than previous UV LFs. We select quasars at z ~ 5 with a deep learning technique from deep data taken by the Hyper Suprime-Cam Subaru Strategic Program, covering a 15.5 deg² area. Beyond the traditional color selection method, we improved the quasar selection by training an artificial neural network to distinguish z ~ 5 quasars from nonquasar sources based on their colors and adopting the Bayesian information criterion that can further remove high-redshift galaxies from the quasar sample. When applied to a small sample of spectroscopically identified quasars and galaxies, our method is successful in selecting quasars at ~83% efficiency (5/6) while minimizing the contamination rate of high-redshift galaxies (1/8) by up to three times compared to the selection using color selection alone (3/8). The number of our final quasar candidates with M ₁₄₅₀ < -22.0 mag is 35. Our quasar UV LF down to M ₁₄₅₀ = -22 mag or even fainter (M ₁₄₅₀ = -21 mag) suggests a rather low number density of faint quasars and the faint-end slope of $-{1.6}_{-0.19}^{+0.21}$ , favoring a scenario where quasars play a minor role in ionizing the IGM at high redshift.