Two Dimensional Clustering of Gamma-Ray Bursts using durations and hardness

TL;DR

This study uses a two-dimensional Gaussian mixture model to classify gamma-ray bursts based on duration and hardness, analyzing datasets from BATSE and Fermi-GBM to explore the existence of multiple classes.

Contribution

It applies an advanced statistical method, Extreme Deconvolution, to classify GRBs considering uncertainties, providing insights into the number of distinct GRB classes.

Findings

BATSE data favors two classes with high confidence.

Fermi-GBM data suggests three classes according to AIC.

Results highlight dataset-dependent classification outcomes.

Abstract

Gamma-Ray Bursts (GRBs) have been traditionally divided into two categories: "short" and "long" with durations less than and greater than two seconds, respectively. However, there is a lot of literature (with conflicting results) regarding the existence of a third intermediate class. To investigate this issue, we carry out a two-dimensional classification using the GRB hardness and duration, and also incorporating the uncertainties in both the variables, by using an extension of Gaussian Mixture Model called Extreme Deconvolution (XDGMM). We carry out this analysis on datasets from two detectors, viz. BATSE and Fermi-GBM. We consider the duration and hardness features in log-scale for each of these datasets and determine the best-fit parameters using XDGMM. This is followed by information theoretic criterion-based tests (AIC and BIC) to determine the optimum number of classes. For BATSE, we find that both AIC and BIC show preference for two components with close to decisive and decisive significance, respectively. For Fermi-GBM, AIC shows preference for three components with decisive significance, whereas BIC does not find any significant difference between two and three components. Our analysis codes have been made publicly available.