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Predicting the Reds...
Predicting the Redshift of γ-Ray-loud AGNs Using Supervised Machine Learning
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- Dainotti, Maria Giovanna (author)
- National Astronomical Observatory of Japan,Space Science Institute
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- Bogdan, Malgorzata (author)
- Lund University,Lunds universitet,Statistiska institutionen,Ekonomihögskolan,Department of Statistics,Lund University School of Economics and Management, LUSEM,Wroclaw University
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- Narendra, Aditya (author)
- Jagiellonian University
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- Gibson, Spencer James (author)
- Carnegie Mellon University
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- Miasojedow, Blazej (author)
- University of Warsaw
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- Liodakis, Ioannis (author)
- University of Turku
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- Pollo, Agnieszka (author)
- Jagiellonian University,National Center for Nuclear Research
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- Nelson, Trevor (author)
- University of Massachusetts
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- Wozniak, Kamil (author)
- AGH University of Science and Technology
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- Nguyen, Zooey (author)
- University of California, Los Angeles
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- Larsson, Johan (author)
- Lund University,Lunds universitet,Statistiska institutionen,Ekonomihögskolan,Department of Statistics,Lund University School of Economics and Management, LUSEM
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(creator_code:org_t)
- 2021-10-21
- 2021
- English.
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In: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 920:2
- Related links:
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http://dx.doi.org/10...
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https://iopscience.i...
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Abstract
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- Active galactic nuclei (AGNs) are very powerful galaxies characterized by extremely bright emissions coming from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine their distance to investigate important astrophysical problems, such as the evolution of the early stars and their formation, along with the structure of early galaxies. The redshift determination is challenging because it requires detailed follow-up of multiwavelength observations, often involving various astronomical facilities. Here we employ machine-learning algorithms to estimate redshifts from the observed γ-ray properties and photometric data of γ-ray-loud AGNs from the Fourth Fermi-LAT Catalog. The prediction is obtained with the Superlearner algorithm using a LASSO-selected set of predictors. We obtain a tight correlation, with a Pearson correlation coefficient of 71.3% between the inferred and observed redshifts and an average Δz norm = 11.6 10-4. We stress that, notwithstanding the small sample of γ-ray-loud AGNs, we obtain a reliable predictive model using Superlearner, which is an ensemble of several machine-learning models.
Subject headings
- NATURVETENSKAP -- Fysik -- Astronomi, astrofysik och kosmologi (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences -- Astronomy, Astrophysics and Cosmology (hsv//eng)
Publication and Content Type
- art (subject category)
- ref (subject category)
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- By the author/editor
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Dainotti, Maria ...
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Bogdan, Malgorza ...
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Narendra, Aditya
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Gibson, Spencer ...
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Miasojedow, Blaz ...
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Liodakis, Ioanni ...
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show more...
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Pollo, Agnieszka
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Nelson, Trevor
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Wozniak, Kamil
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Nguyen, Zooey
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Larsson, Johan
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- About the subject
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Physical Science ...
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and Astronomy Astrop ...
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Astrophysical Jo ...
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Lund University