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GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning

Dainotti, Maria Giovanna ; Bhardwaj, Shubham ; Cook, Christopher ; Ange, Joshua ; Lamichhane, Nishan ; Bogdan, Malgorzata LU ; McGee, Monnie ; Nadolsky, Pavel ; Sarkar, Milind and Pollo, Agnieszka , et al. (2025) In Astrophysical Journal, Supplement Series 277(1).
Abstract

Gamma-ray bursts (GRBs) are intense, short-lived bursts of gamma-ray radiation observed up to a high redshift (z ∼ 10) due to their luminosities. Thus, they can serve as cosmological tools to probe the early Universe. However, we need a large sample of high-z GRBs, currently limited due to the difficulty in securing time at the large aperture telescopes. Thus, it is painstaking to determine quickly whether a GRB is high-z or low-z, which hampers the possibility of performing rapid follow-up observations. Previous efforts to distinguish between high- and low-z GRBs using GRB properties and machine learning (ML) have resulted in limited sensitivity. In this study, we aim to improve this classification by employing an ensemble ML method on... (More)

Gamma-ray bursts (GRBs) are intense, short-lived bursts of gamma-ray radiation observed up to a high redshift (z ∼ 10) due to their luminosities. Thus, they can serve as cosmological tools to probe the early Universe. However, we need a large sample of high-z GRBs, currently limited due to the difficulty in securing time at the large aperture telescopes. Thus, it is painstaking to determine quickly whether a GRB is high-z or low-z, which hampers the possibility of performing rapid follow-up observations. Previous efforts to distinguish between high- and low-z GRBs using GRB properties and machine learning (ML) have resulted in limited sensitivity. In this study, we aim to improve this classification by employing an ensemble ML method on 251 GRBs with measured redshifts and plateaus observed by the Neil Gehrels Swift Observatory. Incorporating the plateau phase with the prompt emission, we have employed an ensemble of classification methods to unprecedentedly enhance the sensitivity. Additionally, we investigate the effectiveness of various classification methods using different redshift thresholds, zthreshold = zt at zt = 2.0, 2.5, 3.0, and 3.5. We achieve a sensitivity of 87% and 89% with a balanced sampling for both zt = 3.0 and zt = 3.5, respectively, representing a 9% and 11% increase in the sensitivity over random forest used alone. Overall, the best results are at zt = 3.5, where the difference between the sensitivity of the training set and the test set is the smallest. This enhancement of the proposed method paves the way for new and intriguing follow-up observations of high-z GRBs.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Astrophysical Journal, Supplement Series
volume
277
issue
1
article number
31
publisher
American Astronomical Society
external identifiers
  • scopus:86000757170
ISSN
0067-0049
DOI
10.3847/1538-4365/adafa9
language
English
LU publication?
yes
id
f9ab83cb-94ae-47e3-b0bc-0fd364b8c901
date added to LUP
2025-06-23 10:17:09
date last changed
2025-06-23 10:17:33
@article{f9ab83cb-94ae-47e3-b0bc-0fd364b8c901,
  abstract     = {{<p>Gamma-ray bursts (GRBs) are intense, short-lived bursts of gamma-ray radiation observed up to a high redshift (z ∼ 10) due to their luminosities. Thus, they can serve as cosmological tools to probe the early Universe. However, we need a large sample of high-z GRBs, currently limited due to the difficulty in securing time at the large aperture telescopes. Thus, it is painstaking to determine quickly whether a GRB is high-z or low-z, which hampers the possibility of performing rapid follow-up observations. Previous efforts to distinguish between high- and low-z GRBs using GRB properties and machine learning (ML) have resulted in limited sensitivity. In this study, we aim to improve this classification by employing an ensemble ML method on 251 GRBs with measured redshifts and plateaus observed by the Neil Gehrels Swift Observatory. Incorporating the plateau phase with the prompt emission, we have employed an ensemble of classification methods to unprecedentedly enhance the sensitivity. Additionally, we investigate the effectiveness of various classification methods using different redshift thresholds, z<sub>threshold</sub> = z<sub>t</sub> at z<sub>t</sub> = 2.0, 2.5, 3.0, and 3.5. We achieve a sensitivity of 87% and 89% with a balanced sampling for both z<sub>t</sub> = 3.0 and z<sub>t</sub> = 3.5, respectively, representing a 9% and 11% increase in the sensitivity over random forest used alone. Overall, the best results are at z<sub>t</sub> = 3.5, where the difference between the sensitivity of the training set and the test set is the smallest. This enhancement of the proposed method paves the way for new and intriguing follow-up observations of high-z GRBs.</p>}},
  author       = {{Dainotti, Maria Giovanna and Bhardwaj, Shubham and Cook, Christopher and Ange, Joshua and Lamichhane, Nishan and Bogdan, Malgorzata and McGee, Monnie and Nadolsky, Pavel and Sarkar, Milind and Pollo, Agnieszka and Nagataki, Shigehiro}},
  issn         = {{0067-0049}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{American Astronomical Society}},
  series       = {{Astrophysical Journal, Supplement Series}},
  title        = {{GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning}},
  url          = {{http://dx.doi.org/10.3847/1538-4365/adafa9}},
  doi          = {{10.3847/1538-4365/adafa9}},
  volume       = {{277}},
  year         = {{2025}},
}