September 30, 2025Astronomy and Astrophysics

Unsupervised machine learning classification of gamma-ray bursts based on the rest-frame prompt emission parameters

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Machine learning techniques classified gamma-ray bursts into GRBs-I and GRBs-II, improving accuracy in identifying progenitors.
Using algorithms t-SNE and UMAP, the study divided GRBs based on rest-frame prompt emission parameters.
Comparison with conventional methods showed enhanced discriminative power, especially for ambiguous hybrid cases.
Clear separation of GRB types was observed, indicating that afterglow follow-up may not be necessary for progenitor classification.

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Implication

Unsupervised machine learning classifies gamma-ray bursts effectively, suggesting improved accuracy in progenitor predictions without afterglow analysis.

Cite This Study

Zhu et al. (2025) studied this question.

synapsesocial.com/papers/68dc1e3f8a7d58c25ebb1ef4 https://doi.org/10.1051/0004-6361/202556274

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Implication

Unsupervised machine learning classifies gamma-ray bursts effectively, suggesting improved accuracy in progenitor predictions without afterglow analysis.

Unsupervised machine learning classification of gamma-ray bursts based on the rest-frame prompt emission parameters

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Discussion

Implication

Cite This Study

Unsupervised machine learning classification of gamma-ray bursts based on the rest-frame prompt emission parameters

Key Points

Discussion

Implication

Cite This Study

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