The quasar luminosity function at $z\sim5$ via deep learning and Bayesian information criterion Article Swipe
Suhyun Shin
,
Myungshin Im
,
Yongjung Kim
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.00570
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.00570
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 $M_{1450} \sim -22.0$ AB mag at $z\sim5$, about one magnitude deeper than previous UV LFs. We select quasars at $z\sim5$ with a deep learning technique from deep data taken by the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), covering a 15.5 deg$^2$ area. Beyond the traditional color selection method, we improved the quasar selection by training an artificial neural network for distinguishing $z\sim5$ 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 $\sim83 \%$ 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 $-1.6^{+0.21}_{-0.19}$, favoring a scenario where quasars play a minor role in ionizing the IGM at high redshift.
Related Topics
Concepts
Quasar
Astrophysics
Physics
Redshift
Luminosity function
Galaxy
Luminosity
Reionization
OVV quasar
Astronomy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2208.00570
- https://arxiv.org/pdf/2208.00570
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4289550808
All OpenAlex metadata
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https://openalex.org/W4289550808Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2208.00570Digital Object Identifier
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The quasar luminosity function at $z\sim5$ via deep learning and Bayesian information criterionWork title
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preprintOpenAlex work type
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enPrimary language
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2022Year of publication
- Publication date
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2022-08-01Full publication date if available
- Authors
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Suhyun Shin, Myungshin Im, Yongjung KimList of authors in order
- Landing page
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https://arxiv.org/abs/2208.00570Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2208.00570Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2208.00570Direct OA link when available
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Quasar, Astrophysics, Physics, Redshift, Luminosity function, Galaxy, Luminosity, Reionization, OVV quasar, AstronomyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Suprime-Cam | 79 |
| abstract_inverted_index.information | 121 |
| abstract_inverted_index.traditional | 91 |
| abstract_inverted_index.ultraviolet | 27 |
| abstract_inverted_index.constraining | 26 |
| abstract_inverted_index.Understanding | 0 |
| abstract_inverted_index.contamination | 160 |
| abstract_inverted_index.high-redshift | 127, 163 |
| abstract_inverted_index.intergalactic | 34 |
| abstract_inverted_index.distinguishing | 107 |
| abstract_inverted_index.spectroscopically | 140 |
| abstract_inverted_index.$-1.6^{+0.21}_{-0.19}$, | 225 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.08665645 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |