A Deep Learning Method for AGILE-GRID Gamma-Ray Burst Detection

Journal
Date Issued
2021
Author(s)
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Longo, F.
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Beneventano, D.
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Macaluso, A.
DOI
Abstract
The 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.
Volume
914
Issue
1
Start page
67
Uri
Url
Issn Identifier
0004-637X
Ads BibCode
2021ApJ...914...67P
Rights
open.access
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