Label Propagation for Identifying Gamma-Ray Burst Progenitors from Prompt Emission
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.
Keywords
Cite
@article{arxiv.2604.26135,
title = {Label Propagation for Identifying Gamma-Ray Burst Progenitors from Prompt Emission},
author = {Skye Strain and Nicoló Cibrario and Michela Negro and Eric Burns},
journal= {arXiv preprint arXiv:2604.26135},
year = {2026}
}
Comments
Submitted to RNAAS. We acknowledge support from LaSPACE under the LURA grant. There is an interactive version of the plot available here: https://grb-smartwaterfall-explorer.vercel.app/