Inferring the redshift of more than 150 GRBs with a machine-learning ensemble model

2024
journal article
article
cris.lastimport.wos2024-04-10T01:47:36Z
dc.abstract.enGamma-ray bursts (GRBs), due to their high luminosities, are detected up to a redshift of 10, and thus have the potential to be vital cosmological probes of early processes in the Universe. Fulfilling this potential requires a large sample of GRBs with known redshifts, but due to observational limitations, only 11% have known redshifts (z). There have been numerous attempts to estimate redshifts via correlation studies, most of which have led to inaccurate predictions. To overcome this, we estimated GRB redshift via an ensemble-supervised machine-learning (ML) model that uses X-ray afterglows of long-duration GRBs observed by the Neil Gehrels Swift Observatory. The estimated redshifts are strongly correlated (a Pearson coefficient of 0.93) and have an rms error, namely, the square root of the average squared error 〈Δz$^{2}$〉, of 0.46 with the observed redshifts showing the reliability of this method. The addition of GRB afterglow parameters improves the predictions considerably by 63% compared to previous results in peer-reviewed literature. Finally, we use our ML model to infer the redshifts of 154 GRBs, which increase the known redshifts of long GRBs with plateaus by 94%, a significant milestone for enhancing GRB population studies that require large samples with redshift.pl
dc.affiliationWydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut – Obserwatorium Astronomicznepl
dc.affiliationSzkoła Doktorska Nauk Ścisłych i Przyrodniczychpl
dc.contributor.authorDainotti, Maria - 174440 pl
dc.contributor.authorTaira, Eliaspl
dc.contributor.authorWang, Ericpl
dc.contributor.authorLehman, Eliaspl
dc.contributor.authorNarendra, Aditya - 431793 pl
dc.contributor.authorPollo, Agnieszka - 131503 pl
dc.contributor.authorMadejski, Grzegorz M.pl
dc.contributor.authorPetrosian, Vahepl
dc.contributor.authorBogdan, Malgorzatapl
dc.contributor.authorDey, Apratimpl
dc.contributor.authorBhardwaj, Shubhampl
dc.date.accessioned2024-03-08T14:30:45Z
dc.date.available2024-03-08T14:30:45Z
dc.date.issued2024pl
dc.date.openaccess0
dc.description.accesstimew momencie opublikowania
dc.description.number1pl
dc.description.versionostateczna wersja wydawcy
dc.description.volume271pl
dc.identifier.articleid22pl
dc.identifier.doi10.3847/1538-4365/ad1aafpl
dc.identifier.eissn1538-4365pl
dc.identifier.issn0067-0049pl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/327773
dc.languageengpl
dc.language.containerengpl
dc.rightsUdzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa*
dc.rights.licenceCC-BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/legalcode.pl*
dc.share.typeotwarte czasopismo
dc.subject.engamma-ray burstspl
dc.subject.enredshift surveyspl
dc.subject.encomputational methodspl
dc.subtypeArticlepl
dc.titleInferring the redshift of more than 150 GRBs with a machine-learning ensemble modelpl
dc.title.journalThe Astrophysical Journal. Supplement Seriespl
dc.typeJournalArticlepl
dspace.entity.typePublication
cris.lastimport.wos
2024-04-10T01:47:36Z
dc.abstract.enpl
Gamma-ray bursts (GRBs), due to their high luminosities, are detected up to a redshift of 10, and thus have the potential to be vital cosmological probes of early processes in the Universe. Fulfilling this potential requires a large sample of GRBs with known redshifts, but due to observational limitations, only 11% have known redshifts (z). There have been numerous attempts to estimate redshifts via correlation studies, most of which have led to inaccurate predictions. To overcome this, we estimated GRB redshift via an ensemble-supervised machine-learning (ML) model that uses X-ray afterglows of long-duration GRBs observed by the Neil Gehrels Swift Observatory. The estimated redshifts are strongly correlated (a Pearson coefficient of 0.93) and have an rms error, namely, the square root of the average squared error 〈Δz$^{2}$〉, of 0.46 with the observed redshifts showing the reliability of this method. The addition of GRB afterglow parameters improves the predictions considerably by 63% compared to previous results in peer-reviewed literature. Finally, we use our ML model to infer the redshifts of 154 GRBs, which increase the known redshifts of long GRBs with plateaus by 94%, a significant milestone for enhancing GRB population studies that require large samples with redshift.
dc.affiliationpl
Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut – Obserwatorium Astronomiczne
dc.affiliationpl
Szkoła Doktorska Nauk Ścisłych i Przyrodniczych
dc.contributor.authorpl
Dainotti, Maria - 174440
dc.contributor.authorpl
Taira, Elias
dc.contributor.authorpl
Wang, Eric
dc.contributor.authorpl
Lehman, Elias
dc.contributor.authorpl
Narendra, Aditya - 431793
dc.contributor.authorpl
Pollo, Agnieszka - 131503
dc.contributor.authorpl
Madejski, Grzegorz M.
dc.contributor.authorpl
Petrosian, Vahe
dc.contributor.authorpl
Bogdan, Malgorzata
dc.contributor.authorpl
Dey, Apratim
dc.contributor.authorpl
Bhardwaj, Shubham
dc.date.accessioned
2024-03-08T14:30:45Z
dc.date.available
2024-03-08T14:30:45Z
dc.date.issuedpl
2024
dc.date.openaccess
0
dc.description.accesstime
w momencie opublikowania
dc.description.numberpl
1
dc.description.version
ostateczna wersja wydawcy
dc.description.volumepl
271
dc.identifier.articleidpl
22
dc.identifier.doipl
10.3847/1538-4365/ad1aaf
dc.identifier.eissnpl
1538-4365
dc.identifier.issnpl
0067-0049
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/327773
dc.languagepl
eng
dc.language.containerpl
eng
dc.rights*
Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa
dc.rights.licence
CC-BY
dc.rights.uri*
http://creativecommons.org/licenses/by/4.0/legalcode.pl
dc.share.type
otwarte czasopismo
dc.subject.enpl
gamma-ray bursts
dc.subject.enpl
redshift surveys
dc.subject.enpl
computational methods
dc.subtypepl
Article
dc.titlepl
Inferring the redshift of more than 150 GRBs with a machine-learning ensemble model
dc.title.journalpl
The Astrophysical Journal. Supplement Series
dc.typepl
JournalArticle
dspace.entity.type
Publication
Affiliations

* The migration of download and view statistics prior to the date of April 8, 2024 is in progress.

Views
1
Views per month
Views per city
Krakow
1