The quasar luminosity function at via deep learning and Bayesian information criterion
arXiv:2208.00570 · doi:10.3847/1538-4357/ac854b
Abstract
Understanding the faint end of quasar luminosity function 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 AB mag at , about one magnitude deeper than previous UV LFs. We select quasars at with a deep learning technique from deep data taken by the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), covering a 15.5 deg area. Beyond the traditional color selection method, we improved the quasar selection by training an artificial neural network for distinguishing quasars from non-quasar 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 efficiency () while minimizing the contamination rate of high-redshift galaxies () by up to three times compared to the selection using color selection alone (). The number of our final quasar candidates with mag is 35. Our quasar UV LF down to mag or even fainter ( mag) suggests a rather low number density of faint quasars and the faint-end slope of , favoring a scenario where quasars play a minor role in ionizing the IGM at high redshift.
19 pages, 8 figures, Accepted for publication in ApJ
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