Advancing Identification method of Gamma-Ray Bursts with Data and Feature Enhancement

TL;DR

This paper introduces a novel neural network framework with data augmentation for improved gamma-ray burst identification, achieving high accuracy and revealing physically meaningful features that could enhance multi-messenger astronomy.

Contribution

It presents a new CNN-based method with physics-informed data augmentation and feature enhancement, significantly improving GRB classification and physical interpretability.

Findings

Achieved 97.46% classification accuracy.

Generated 100,000 synthetic GRB samples.

Revealed physically meaningful features linked to progenitor origins.

Abstract

Gamma-ray bursts (GRBs) are challenging to identify due to their transient nature, complex temporal profiles, and limited observational datasets. We address this with a one-dimensional convolutional neural network integrated with an Adaptive Frequency Feature Enhancement module and physics-informed data augmentation. Our framework generates 100,000 synthetic GRB samples, expanding training data diversity and volume while preserving physical fidelity-especially for low-significance events. The model achieves 97.46% classification accuracy, outperforming all tested variants with conventional enhancement modules, highlighting enhanced domain-specific feature capture. Feature visualization shows model focuses on deep-seated morphological features and confirms the capability of extracting physically meaningful burst characteristics. Dimensionality reduction and clustering reveal GRBs with similar morphologies or progenitor origins cluster in the feature space, linking learned features to physical properties. This perhaps offers a novel diagnostic tool for identifying kilonova- and supernova-associated GRB candidates, establishing criteria to enhance multi-messenger early-warning systems. The framework aids current time-domain surveys, generalizes to other rare transients, and advances automated detection in large-volume observational data.