Advancing Gamma-Ray Burst Identification through Transfer Learning with Convolutional Neural Networks

TL;DR

This paper introduces a transfer learning approach using CNNs with data augmentation to improve the universal detection of Gamma-Ray Bursts, achieving high accuracy and discovering new GRBs in GECAM-B data.

Contribution

It presents a novel transfer learning framework with a multi-scale CNN model and data augmentation for universal GRB identification, validated on GECAM-B data.

Findings

Achieved 96.41% accuracy on GECAM-B dataset.

Successfully identified three new GRBs.

Enhanced detection performance with transfer learning and data augmentation.

Abstract

The Rapid and accurate identification of Gamma-Ray Bursts (GRBs) is crucial for unraveling their origins. However, current burst search algorithms frequently miss low-threshold signals or lack universality for observations. In this study, we propose a novel approach utilizing transfer learning experiment based on convolutional neural network (CNN) to establish a universal GRB identification method, which validated successfully using GECAM-B data. By employing data augmentation techniques, we enhance the diversity and quantity of the GRB sample. We develop a 1D CNN model with a multi-scale feature cross fusion module (MSCFM) to extract features from samples and perform classification. The comparative results demonstrated significant performance improvements following pre-training and transferring on a large-scale dataset. Our optimal model achieved an impressive accuracy of 96.41% on the source dataset of GECAM-B, and identified three previously undiscovered GRBs by contrast with manual analysis of GECAM-B observations. These innovative transfer learning and data augmentation methods presented in this work hold promise for applications in multi-satellite exploration scenarios characterized by limited data sets and a scarcity of labeled samples in high-energy astronomy.