A Tight Three-parameter Correlation and Related Classification on Gamma-Ray Bursts
Shuai Zhang, Lang Shao, Bin-Bin Zhang, Jin-Hang Zou, Hai-Yuan Sun,, Yu-Jie Yao, Lin-Lin Li

TL;DR
This paper identifies a precise three-parameter correlation in gamma-ray burst data and introduces a new classification method that improves the distinction between long and short GRBs, revealing potential subclasses.
Contribution
It presents a novel three-parameter correlation and a linear discriminant-based parameter for better GRB classification, along with evidence for subclasses within long GRBs.
Findings
A tight correlation between fluence, peak flux, and duration was established.
A new parameter effectively distinguishes long and short GRBs with less ambiguity.
Long GRBs may be divided into bright and faint subclasses with different properties.
Abstract
Gamma-ray bursts (GRBs) are widely believed to be from massive collapsars and/or compact binary mergers, which accordingly, would generate long and short GRBs, respectively. The details on this classification scheme have been in constant debate given more and more observational data available to us. In this work, we apply a series of data mining methods to studying the potential classification information contained in the prompt emission of GRBs detected by the Fermi Gamma-ray Burst Monitor. A tight global correlation is found between fluence (), peak flux () and prompt duration () which takes the form of . Based on this correlation, we can define a new parameter by linear discriminant analysis that would distinguish between long and short GRBs with much less…
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