Label Propagation for Identifying Gamma-Ray Burst Progenitors from Prompt Emission
arXiv:2604.26135 · doi:10.3847/2515-5172/ae6b74
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.
arXiv commentSubmitted 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/
References in corpus (15)
- Gravitational Waves and Gamma-rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A
- The Fermi Gamma-Ray Burst Monitor
- A Kilonova Following a Long-Duration Gamma-Ray Burst at 350 Mpc
- The Fourth Fermi-GBM Gamma-Ray Burst Catalog: A Decade of Data
- The Observer's Guide to the Gamma-Ray Burst-Supernova Connection
- JWST detection of heavy neutron capture elements in a compact object merger
- A bright gamma-ray flare interpreted as a giant magnetar flare in NGC 253
- Identification of a Local Sample of Gamma-Ray Bursts Consistent with a Magnetar Giant Flare Origin
- Gaussian-Mixture-Model-based Cluster Analysis Finds Five Kinds of Gamma Ray Bursts in the BATSE Catalog
- GRB171010A / SN2017htp: a GRB-SN at z=0.33
- Evidence for two distinct populations of kilonova-associated Gamma Ray Bursts
- The supernova of the MAGIC GRB190114C
- On The Origin of Supernova-Less Long Gamma Ray Bursts
- Prompt GRB recognition through waterfalls and deep learning
- Embedding Reliability for Unsupervised Classification of Gamma Ray Burst progenitors from Prompt Gamma-ray Emission