Label Propagation for Identifying Gamma-Ray Burst Progenitors from Prompt Emission

TL;DR

This paper explores a semi-supervised label propagation algorithm to classify gamma-ray burst progenitors, aiming to enhance follow-up observations and understanding of GRB physics.

Contribution

It introduces a semi-supervised machine learning approach using label propagation for classifying GRB progenitors from prompt emission data.

Findings

Evaluated on 2512 GRBs, demonstrating probabilistic classification capabilities.

Potential for improved progenitor identification with further dataset and method refinement.

Lays groundwork for real-time GRB progenitor classification.

Abstract

Gamma-ray bursts (GRBs) are the most energetic bursts of light in our universe, and rapid progenitor association of these events can lead to targeted and optimized follow-up observations, ultimately providing better insights about the physics involved. In this note, we investigate a semi-supervised machine learning algorithm, that utilizes label propagation, as a classification method. Using a dataset of 2512 GRBs we evaluate the method's ability to assign probabilistic class memberships based on a subset of events with known progenitors. Further analysis is ongoing to improve the method and future progress will be made to refine the classification algorithm and the dataset.