Identifying Merger-Driven Long Gamma-Ray Bursts based on Machine Learning
Si-Yuan Zhu, Hui-Ying Deng, Fu-Wen Zhang, Qian-Zi Mo, Pak-Hin Thomas Tam

TL;DR
This study uses machine learning to identify a subclass of long gamma-ray bursts (Type IL) based on prompt emission features, enabling rapid classification and revealing their distinct properties and potential merger origins.
Contribution
The paper introduces a machine learning approach to classify Type IL GRBs from prompt emission data, expanding the identification of merger-driven long GRBs beyond afterglow observations.
Findings
Identified 29 three-episode GRBs in the Fermi/GBM catalog.
Discovered six new Type IL GRBs using machine learning classification.
Characterized Type IL GRBs by short durations, minimal variability, and specific spectral correlations.
Abstract
Gamma-ray bursts (GRBs) are classified as Type I GRBs originated from compact binary mergers and Type II GRBs originated from massive collapsars. While Type I GRBs are typically shorter than 2 seconds, recent observations suggest that some extend to tens of seconds, forming a potential subclass, Type IL GRBs. However, apart from their association with kilonovae, so far no rapid identification is possible. Given the uncertainties and limitations of optical and infrared afterglow observations, an identification method based solely on prompt emission can make such identification possible for many more GRBs. Interestingly, two established Type IL GRBs: GRB 211211A and GRB 230307A, exhibit a three-episode structure: precursor emission (PE), main emission (ME), and extended emission. Therefore, we comprehensively search for GRBs in the Fermi/GBM catalog and identify 29 three-episode GRBs.…
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Taxonomy
TopicsGamma-ray bursts and supernovae · Alexander von Humboldt Studies · Astronomy and Astrophysical Research
