Identifying the Physical Origin of Gamma-Ray Bursts with Supervised Machine Learning

Luo, Jia-Wei; Wang, Fei-Fei; Zhu-Ge, Jia-Ming; Li, Ye; Zou, Yuan-Chuan; Zhang, Bing

doi:10.3847/1538-4357/ad03ec

Cited by 4 publications

(1 citation statement)

References 144 publications

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“…Specifically, the large databases from Fermi and BATSE, such as that utilized in this work, do not possess the requisite sample size with this multicriteria information for such analyses. Nevertheless, it is noteworthy that studies have been undertaken employing multiple criteria for smaller sample sizes and supervised clustering techniques to investigate the physical origins of GRBs, as evidenced by previous works such as those of Zhang et al (2009), Li et al (2020), andLuo et al (2023).…”

Section: Introductionmentioning

confidence: 99%

Exploring Gamma-Ray Burst Diversity: Clustering Analysis of the Emission Characteristics of Fermi- and BATSE-detected Gamma-Ray Bursts

Mehta,

Iyyani

2024

ApJ

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Gamma-ray bursts (GRBs), often attributed to massive star collapse or binary compact object mergers, exhibit diverse emission characteristics hinting at multiple GRB classes based on various factors like progenitors, radiation mechanisms, and central engines. This study employs unsupervised clustering using the nested Gaussian mixture model algorithm to analyze data from Fermi and BATSE, identifying four classes (A–D) based on duration, spectral peak, and spectral index of time-integrated spectra of GRBs. Class proportions are approximately 70%, 10%, 3%, and 17%, respectively, with A and B comprising mostly long GRBs, C mainly short GRBs, and D encompassing both types. The classes are further assessed based on spectral index α, indicating distinct radiation mechanisms: α > −0.67 for photospheric emission, α ≤ −1.5 for fast-cooling synchrotron, and −1.5 < α ≤ −0.67 for slow-cooling synchrotron. Classes B and C align with photospheric emission, while A and D predominantly exhibit synchrotron radiation. Short GRBs are predominantly photospheric emission, whereas long GRBs tend to favor synchrotron emission. Overall, 63% of the total bursts exhibit α profiles indicative of synchrotron emission, with the remaining 37% associated with photospheric emission. Considering the limited data of kilonova and supernova associated with GRBs, classes are examined for progenitor origins, suggesting a hybrid nature for A and D, and collapsar and merger origins for B and C, respectively. This clustering analysis results in four GRB classes, which, upon investigation, reveal the diverse and complex nature of GRBs in terms of their radiation, duration, and progenitor.

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Section: Introductionmentioning

confidence: 99%

Exploring Gamma-Ray Burst Diversity: Clustering Analysis of the Emission Characteristics of Fermi- and BATSE-detected Gamma-Ray Bursts

Mehta,

Iyyani

2024

ApJ

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Classification of Fermi gamma-ray bursts based on machine learning

Zhu,

Sun,

et al. 2024

Monthly Notices of the Royal Astronomical Society

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Gamma-ray bursts (GRBs) are typically classified into long and short GRBs based on their durations. However, there is a significant overlapping in the duration distributions of these two categories. In this paper, we apply the unsupervised dimensionality reduction algorithm called t-SNE and UMAP to classify 2061 Fermi GRBs based on four observed quantities: duration, peak energy, fluence, and peak flux. The map results of t-SNE and UMAP show a clear division of these GRBs into two clusters. We mark the two clusters as GRBs-I and GRBs-II, and find that all GRBs associated with supernovae are classified as GRBs-II. It includes the peculiar short GRB 200826A, which was confirmed to originate from the death of a massive star. Furthermore, except for two extreme events GRB 211211A and GRB 230307A, all GRBs associated with kilonovae fall into GRBs-I population. By comparing to the traditional classification of short and long GRBs, the distribution of durations for GRBs-I and GRBs-II do not have a fixed boundary. We find that more than 10 per cent of GRBs-I have a duration greater than 2 s, while approximately 1 per cent of GRBs-II have a duration shorter than 2 s.

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GRB Progenitor Classification from Gamma-Ray Burst Prompt and Afterglow Observations

Nuessle,

Racusin,

White

2024

ApJ

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Using an established classification technique, we leverage standard observations and analyses to predict the progenitors of gamma-ray bursts (GRBs). This technique, utilizing support vector machine statistics, provides a more nuanced prediction than the previous two-component Gaussian mixture in duration of the prompt gamma-ray emission. Based on further covariance testing from Fermi/Gamma Ray Burst Monitor, Swift/Burst Alert Telescope, and Swift/X-Ray Telescope data, we find that our classification based only on prompt emission properties gives perspective on the recent evidence that mergers and collapsars exist in both “long” and “short” GRB populations.

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Identifying the Physical Origin of Gamma-Ray Bursts with Supervised Machine Learning

Cited by 4 publications

References 144 publications

Exploring Gamma-Ray Burst Diversity: Clustering Analysis of the Emission Characteristics of Fermi- and BATSE-detected Gamma-Ray Bursts

Exploring Gamma-Ray Burst Diversity: Clustering Analysis of the Emission Characteristics of Fermi- and BATSE-detected Gamma-Ray Bursts

Classification of Fermi gamma-ray bursts based on machine learning

GRB Progenitor Classification from Gamma-Ray Burst Prompt and Afterglow Observations

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