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Predicting the redshift of $\gamma$-ray-loud AGNs using supervised machine learning

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dc.abstract.enActive 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<br />and their formation, along with the structure of early galaxies. The redshift determination is challenging because itrequires 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 Δznorm =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.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorBogdan, Małgorzata
dc.contributor.authorDainotti, Maria Giovanna
dc.contributor.authorNarendra, Aditya
dc.contributor.authorGibson, Spencer James
dc.contributor.authorLiodakis, Ioannis
dc.contributor.authorPollo, Agnieszka
dc.contributor.authorNelson, Trevor
dc.contributor.authorWozniak, Kamil
dc.contributor.authorNguyen, Zooey
dc.contributor.authorLarrson, Johan
dc.contributor.authorMiasojedow, Błażej
dc.date.accessioned2024-01-25T17:32:35Z
dc.date.available2024-01-25T17:32:35Z
dc.date.copyright2021-12-07
dc.date.issued2021
dc.date.openaccess12
dc.description.accesstimeAFTER_PUBLICATION
dc.description.financePublikacja bezkosztowa
dc.description.number2
dc.description.versionFINAL_PUBLISHED
dc.description.volume920
dc.identifier.doi10.3847/1538-4357/AC1748
dc.identifier.issn0004-637X
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/116909
dc.identifier.weblinkhttps://ruj.uj.edu.pl/xmlui/handle/item/284750
dc.languageeng
dc.pbn.affiliationmathemathics
dc.relation.ispartofAstrophysical Journal
dc.rightsCC-BY
dc.sciencecloudnosend
dc.titlePredicting the redshift of $\gamma$-ray-loud AGNs using supervised machine learning
dc.typeJournalArticle
dspace.entity.typePublication