Identifying Merger-Driven and Collapsar-Driven Gamma-Ray Bursts with Precursor based Solely on Prompt Emission
Si-Yuan Zhu, Pak-Hin Thomas Tam, Fu-Wen Zhang, Hui-Ying Deng, Bing Zhang

TL;DR
This study uses machine learning on Fermi/GBM data to distinguish merger-driven and collapsar-driven gamma-ray bursts based solely on prompt emission features, introducing a new diagnostic parameter.
Contribution
It introduces the $E_{ m p,ME}$-precursor index ($EPI$), a novel diagnostic tool for identifying GRB origins from prompt emission data alone.
Findings
Unsupervised machine learning successfully distinguishes GRB types.
The $EPI$ parameter effectively classifies GRBs as merger- or collapsar-driven.
Validation suggests applicability to other instruments like Swift.
Abstract
Gamma-ray bursts (GRBs) are generally classified as Type~I GRBs, which originate from compact binary mergers, and Type~II GRBs, which originate from massive collapsars. The traditional correspondence between short--Type~I GRBs and long--Type~II GRBs, separated by a duration of 2 seconds, has been challenged by recent observations of long GRBs associated with kilonovae (i.e., Type~I-L GRBs) and a short GRB associated with a supernova. In this paper, we focus on GRBs with precursor emission (PE) and compile 366 GRBs detected by Fermi/GBM. Applying the unsupervised machine learning methods t-SNE and UMAP, we are able to distinguish Type~I (including subclass Type~I-L) and Type~II GRBs for the first time and identify PE as a key feature for distinguishing GRBs of different origins. Inspired by results of machine learning, we propose a diagnostic parameter, the -precursor index…
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