GRB Progenitor Classification from Gamma-Ray Burst Prompt and Afterglow Observations

TL;DR

This paper introduces a support vector machine-based classification method for gamma-ray burst progenitors, improving prediction accuracy over previous models by analyzing prompt and afterglow observations from multiple space telescopes.

Contribution

It presents a novel SVM-based classification approach that enhances GRB progenitor prediction using standard observational data and covariance testing across different instruments.

Findings

Supports the existence of both mergers and collapsars in long and short GRB populations.

Provides a more nuanced classification than previous Gaussian mixture models.

Demonstrates the effectiveness of prompt emission properties in progenitor prediction.

Abstract

Using an established classification technique, we leverage standard observations and analyses to predict the progenitors of gamma-ray bursts (GRBs). This technique, utilizing support vector machine (SVM) statistics, provides a more nuanced prediction than the previous two-component Gaussian mixture in duration of the prompt gamma-ray emission. Based on further covariance testing from \textit{Fermi}-GBM, \textit{Swift}-BAT, and \textit{Swift}-XRT data, we find that our classification based only on prompt emission properties gives perspective on the recent evidence that mergers and collapsars exist in both long and short GRB populations.