PARMIGGIANI, NICOLONICOLOPARMIGGIANIBULGARELLI, ANDREAANDREABULGARELLIFIORETTI, VALENTINAVALENTINAFIORETTIDI PIANO, AMBRAAMBRADI PIANOGIULIANI, ANDREAANDREAGIULIANILongo, F.F.LongoVERRECCHIA, FrancescoFrancescoVERRECCHIATAVANI, MARCOMARCOTAVANIBeneventano, D.D.BeneventanoMacaluso, A.A.Macaluso2023-04-182023-04-1820210004-637Xhttp://hdl.handle.net/20.500.12386/34085The follow-up of external science alerts received from gamma-ray burst (GRB) and gravitational wave detectors is one of the AGILE Team's current major activities. The AGILE team developed an automated real-time analysis pipeline to analyze 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 by improving the GRB detection capability over the Li & Ma method, currently used by the AGILE team. The CNN is trained with large simulated data sets of intensity maps. The AGILE complex observing pattern due to the so-called "spinning mode" is studied to prepare data sets to test and evaluate the CNN. A GRB emission model is defined from the second Fermi-LAT GRB catalog and convoluted with the AGILE observing pattern. Different p-value distributions are calculated, evaluating, using the CNN, millions of background-only maps simulated by varying the background level. The CNN is then used on real data to analyze 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 catalogs. From these catalogs, the CNN detects 21 GRBs with a significance of ≥3&sgr;, 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.STAMPAenArticle10.3847/1538-4357/abfa152-s2.0-85108986240https://iopscience.iop.org/article/10.3847/1538-4357/abfa15https://api.elsevier.com/content/abstract/scopus_id/851089862402021ApJ...914...67PFIS/05 - ASTRONOMIA E ASTROFISICA