Classifying Gamma-Ray Bursts with Gaussian Mixture Model

2016/03/11 by En-Bo Yang, Zhi-Bin Zhang, Yang, En-Bo +5
Physics and Astronomy · #FOS: Physical sciences #Gamma-ray bursts and supernovae #High Energy Astrophysical Phenomena (astro-ph.HE)

paper · pdf · doi:10.48550/arxiv.1603.03680

openalex publication_date 2016/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Using Gaussian Mixture Model (GMM) and Expectation Maximization Algorithm, we perform an analysis of time duration (T₉₀) for CGRO/BATSE, Swift/BAT and Fermi/GBM Gamma-Ray Bursts. The T₉₀ distributions of 298 redshift-known Swift/BAT GRBs have also been studied in both observer and rest frames. Bayesian Information Criterion has been used to compare between different GMM models. We find that two Gaussian components are better to describe the CGRO/BATSE and Fermi/GBM GRBs in the observer frame. Also, we caution that two groups are expected for the Swift/BAT bursts in the rest frame, which is consistent with some previous results. However, Swift GRBs in the observer frame seem to show a trimodal distribution, of which the superficial intermediate class may result from the selection effect of Swift/BAT.

Related

• The classification and formation rate of Swift/BAT gamma-ray bursts
• SWIFT and BATSE bursts' classification
• Generalized Linear Models of T₉₀-T₅₀ relation to classify GRBs
• Gamma-ray burst taxonomy: looking for the third class on the spectral peak energy-duration plane in the rest frame
• GRB Progenitor Classification from Gamma-Ray Burst Prompt and Afterglow Observations