A Deep Learning Method for AGILE-GRID GRB Detection.

The follow-up of external science alerts received from Gamma-Ray Bursts (GRB) and Gravitational Waves (GW) detectors is one of the AGILE Team's current major activities. The AGILE team developed an automated real-time analysis pipeline to analyse AGILE Gamma-Ray Imaging Detector (GRID) data to detect possible counterparts in the energy range 0.1-10 GeV. This work presents a new approach for detecting GRBs using a Convolutional Neural Network (CNN) to classify the AGILE-GRID intensity maps improving the GRBs detection capability over the Li&Ma method, currently used by the AGILE team. The CNN is trained with large simulated datasets of intensity maps. The AGILE complex observing pattern due to the so-called 'spinning mode' is studied to prepare datasets to test and evaluate the CNN. A GRB emission model is defined from the Second Fermi-LAT GRB catalogue and convoluted with the AGILE observing pattern. Different p-value distributions are calculated evaluating with the CNN millions of background-only maps simulated varying the background level. The CNN is then used on real data to analyse the AGILE-GRID data archive, searching for GRB detections using the trigger time and position taken from the Swift-BAT, Fermi-GBM, and Fermi-LAT GRB catalogues. From these catalogues, the CNN detects 21 GRBs with a significance $\geq 3 \sigma$, while the Li&Ma method detects only two GRBs. The results shown in this work demonstrate that the CNN is more effective in detecting GRBs than the Li&Ma method in this context and can be implemented into the AGILE-GRID real-time analysis pipeline.