Prompt GRB recognition through waterfalls and deep learning

TL;DR

This paper introduces a novel deep learning approach using 'waterfalls' data representations for rapid gamma-ray burst classification, aiming to improve prompt identification and follow-up observations.

Contribution

It presents a new self-supervised deep learning method utilizing 'waterfalls' data for GRB classification, advancing beyond traditional techniques.

Findings

Effective classification accuracy demonstrated

Waterfalls data enhances model performance

Potential for real-time GRB detection

Abstract

Gamma-ray Bursts (GRBs) are one of the most energetic phenomena in the cosmos, whose study probes physics extremes beyond the reach of laboratories on Earth. Our quest to unravel the origin of these events and understand their underlying physics is far from complete. Central to this pursuit is the rapid classification of GRBs to guide follow-up observations and analysis across the electromagnetic spectrum and beyond. Here, we introduce a compelling approach that can set milestone towards a new and robust GRB prompt classification method. Leveraging self-supervised deep learning, we pioneer a previously unexplored data product to approach this task: the GRB waterfalls.