Multivariate $t$-Mixtures-Model-based Cluster Analysis of BATSE Catalog Establishes Importance of All Observed Parameters, Confirms Five Distinct Ellipsoidal Sub-populations of Gamma Ray Bursts
Souradeep Chattopadhyay, Ranjan Maitra

TL;DR
This study uses $t$-mixture models to classify 1599 BATSE gamma-ray bursts into five distinct, well-defined groups based on all observed parameters, refining previous classifications and including incomplete data in the analysis.
Contribution
It introduces a $t$-mixture model-based clustering approach that considers all observed parameters and classifies GRBs into five distinct groups, improving upon prior methods.
Findings
Identified five distinct GRB groups with clear parameter differences.
Refined previous classifications with more distinct groupings.
Successfully classified incomplete data into established groups.
Abstract
Determining the kinds of gamma-ray bursts (GRBs) has been of interest to astronomers for many years. We analyzed 1599 GRBs from the Burst and Transient Source Experiment (BATSE) 4Br catalogue using -mixtures-model-based clustering on all nine observed parameters (, , , , , , , , ) and found evidence of five types of GRBs. Our results further refine the findings of Chattopadhyay and Maitra (2017) by providing groups that are more distinct. Using the Mukherjee et al. (1998) classification scheme, also used by Chattopadhyay and Maitra (2017), of duration, total fluence ()) and spectrum (using Hardness Ratio ) our five groups are classified as long-intermediate-intermediate, short-faint-intermediate, short-faint-soft, long-bright-hard, and long-intermediate-hard. We also…
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