The quasar luminosity function at $z\sim5$ via deep learning and Bayesian information criterion
Suhyun Shin, Myungshin Im, Yongjung Kim

TL;DR
This study uses deep learning and Bayesian information criterion to identify faint quasars at high redshift, extending the luminosity function to fainter magnitudes and providing insights into their role in early universe ionization.
Contribution
Introduces a novel quasar selection method combining deep learning and Bayesian information criterion, improving efficiency and reducing contamination in high-redshift quasar surveys.
Findings
Extended quasar luminosity function to $M_{1450} \\sim -22$ mag at $z\\sim5$
Achieved 83% efficiency in quasar selection with low galaxy contamination
Faint-end slope of the quasar luminosity function is approximately -1.6
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Advanced Statistical Methods and Models · Spectroscopy and Chemometric Analyses
