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  5. Two Classes of Gamma-ray Bursts Distinguished within the First Second of Their Prompt Emission
 
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Two Classes of Gamma-ray Bursts Distinguished within the First Second of Their Prompt Emission

Author(s)
Salmon, Lana  
Hanlon, Lorraine  
Martin-Carrillo, Antonio  
Uri
http://hdl.handle.net/10197/13038
Date Issued
2022-06-26
Date Available
2022-08-05T08:47:09Z
Abstract
Studies of Gamma-Ray Burst (GRB) properties, such as duration and spectral hardness, have found evidence for additional classes, beyond the short/hard and long/soft prototypes, using model-dependent methods. In this paper, a model-independent approach was used to analyse the gamma-ray light curves of large samples of GRBs detected by BATSE, Swift/BAT and Fermi/GBM. All the features were extracted from the GRB time profiles in four energy bands using the Stationary Wavelet Transform and Principal Component Analysis. t-distributed Stochastic Neighbourhood Embedding (t-SNE) visualisation of the features revealed two distinct groups of Swift/BAT bursts using the T100 interval with 64 ms resolution data. When the same analysis was applied to 4 ms resolution data, two groups were seen to emerge within the first second (T1) post-trigger. These two groups primarily consisted of short/hard (Group 1) and long/soft (Group 2) bursts, and were 95% consistent with the groups identified using the T100 64 ms resolution data. Kilonova candidates, arising from compact object mergers, were found to belong to Group 1, while those events with associated supernovae fell into Group 2. Differences in cumulative counts between the two groups in the first second, and in the minimum variability timescale, identifiable only with the 4 ms resolution data, may account for this result. Short GRBs have particular significance for multi-messenger science as a distinctive EM signature of a binary merger, which may be discovered by its gravitational wave emissions. Incorporating the T1 interval into classification algorithms may support the rapid classification of GRBs, allowing for an improved prioritisation of targets for follow-up observations.
Sponsorship
European Commission Horizon 2020
Science Foundation Ireland
Other Sponsorship
Irish Research Council Postgraduate Scholarship
Type of Material
Journal Article
Publisher
MDPI
Journal
Galaxies
Volume
10
Issue
4
Start Page
1
End Page
22
Copyright (Published Version)
2022 The Authors
Subjects

Gamma-ray burst

Feature extraction

Machine learning

DOI
10.3390/galaxies10040078
Language
English
Status of Item
Peer reviewed
ISSN
2075-4434
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by/3.0/ie/
File(s)
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FirstSecond_250622.pdf

Size

9.78 MB

Format

Adobe PDF

Checksum (MD5)

d6fd450fa8bb6e8d1a034e2f0b46d533

Owning collection
Physics Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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