Classification of Fermi Gamma-Ray Bursts Based on Machine Learning

TL;DR

This study uses unsupervised machine learning algorithms t-SNE and UMAP to classify Fermi gamma-ray bursts into two distinct clusters, revealing correlations with supernovae and kilonovae associations beyond traditional duration-based classification.

Contribution

The paper introduces a novel application of t-SNE and UMAP for classifying GRBs, providing a more nuanced clustering that correlates with astrophysical phenomena.

Findings

GRBs are clearly divided into two clusters with t-SNE and UMAP.

All supernova-associated GRBs are in the second cluster.

Most kilonova-associated GRBs fall into the first cluster.

Abstract

Gamma-ray bursts (GRBs) are typically classified into long and short GRBs based on their durations. However, there is a significant overlapping in the duration distributions of these two categories. In this paper, we apply the unsupervised dimensionality reduction algorithm called t-SNE and UMAP to classify 2061 Fermi GRBs based on four observed quantities: duration, peak energy, fluence, and peak flux. The map results of t-SNE and UMAP show a clear division of these GRBs into two clusters. We mark the two clusters as GRBs-I and GRBs-II, and find that all GRBs associated with supernovae are classified as GRBs-II. It includes the peculiar short GRB 200826A, which was confirmed to originate from the death of a massive star. Furthermore, except for two extreme events GRB 211211A and GRB 230307A, all GRBs associated with kilonovae fall into GRBs-I population. By comparing to the traditional classification of short and long GRBs, the distribution of durations for GRBs-I and GRBs-II do not have a fixed boundary. We find that more than 10% of GRBs-I have a duration greater than 2 seconds, while approximately 1% of GRBs-II have a duration shorter than 2 seconds.