Identifying GRBs from the Fermi/GBM TTE data

The visualization methods of Grad-CAM and t-SNE explicitly displayed that the optimal model focuses on the key features of GRBs precisely. The model was applied to analyze 1 yr data, accurately identifying approximately 98% of GRBs listed in the Fermi burst catalog, eight out of nine subthreshold GRBs, and five GRBs triggered by other satellites, which demonstrated that the deep- learning methods could effectively distinguish GRBs from observational data.

Besides, thousands of unknown candidates were retrieved and compared with the bursts of SGR J1935+2154, for instance, which exemplified the potential scientific value of these candidates indeed. Detailed studies on integrating our model into real-time analysis pipelines thus may improve their accuracy of inspection and provide valuable guidance for rapid follow-up observations of multiband telescopes.