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Gamma-Ray Bursts as Distance Indicators by a Statistical Learning Approach

Dainotti, Maria Giovanna ; Narendra, Aditya ; Pollo, Agnieszka ; Petrosian, Vahé ; Bogdan, Malgorzata LU ; Iwasaki, Kazunari ; Prochaska, Jason Xavier ; Rinaldi, Enrico and Zhou, David (2024) In Astrophysical Journal Letters 967(2).
Abstract

Gamma-ray bursts (GRBs) can be probes of the early Universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory have known redshifts (z) due to observational limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of 0.93) with the observed redshifts, thus proving the reliability of this method. The inferred and observed redshifts allow us to estimate the number of GRBs occurring at a given redshift (GRB rate) to be 8.47-9 yr−1 Gpc−1 for 1.9 < z < 2.3. Since GRBs come from the... (More)

Gamma-ray bursts (GRBs) can be probes of the early Universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory have known redshifts (z) due to observational limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of 0.93) with the observed redshifts, thus proving the reliability of this method. The inferred and observed redshifts allow us to estimate the number of GRBs occurring at a given redshift (GRB rate) to be 8.47-9 yr−1 Gpc−1 for 1.9 < z < 2.3. Since GRBs come from the collapse of massive stars, we compared this rate with the star formation rate, highlighting a discrepancy of a factor of 3 at z < 1.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Astrophysical Journal Letters
volume
967
issue
2
article number
L30
publisher
IOP Publishing
external identifiers
  • scopus:85194061058
ISSN
2041-8205
DOI
10.3847/2041-8213/ad4970
language
English
LU publication?
yes
id
4c9a4c2b-e772-4654-976a-4e9375055985
date added to LUP
2024-06-05 13:52:55
date last changed
2024-06-05 13:54:07
@article{4c9a4c2b-e772-4654-976a-4e9375055985,
  abstract     = {{<p>Gamma-ray bursts (GRBs) can be probes of the early Universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory have known redshifts (z) due to observational limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of 0.93) with the observed redshifts, thus proving the reliability of this method. The inferred and observed redshifts allow us to estimate the number of GRBs occurring at a given redshift (GRB rate) to be 8.47-9 yr<sup>−1</sup> Gpc<sup>−1</sup> for 1.9 &lt; z &lt; 2.3. Since GRBs come from the collapse of massive stars, we compared this rate with the star formation rate, highlighting a discrepancy of a factor of 3 at z &lt; 1.</p>}},
  author       = {{Dainotti, Maria Giovanna and Narendra, Aditya and Pollo, Agnieszka and Petrosian, Vahé and Bogdan, Malgorzata and Iwasaki, Kazunari and Prochaska, Jason Xavier and Rinaldi, Enrico and Zhou, David}},
  issn         = {{2041-8205}},
  language     = {{eng}},
  month        = {{06}},
  number       = {{2}},
  publisher    = {{IOP Publishing}},
  series       = {{Astrophysical Journal Letters}},
  title        = {{Gamma-Ray Bursts as Distance Indicators by a Statistical Learning Approach}},
  url          = {{http://dx.doi.org/10.3847/2041-8213/ad4970}},
  doi          = {{10.3847/2041-8213/ad4970}},
  volume       = {{967}},
  year         = {{2024}},
}