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

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 ≥3σ, 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.

Gamma-ray Bursts: 50 Years and Counting!

“…More recently, AGILE has had huge success in the application of modern machinelearning techniques for the identification of GRB signals in data from both the gamma-ray imaging detector (GRID) [122] and the anticoincidence system (ACS) [123], leading to the identification of 72 GRB signals with significance ≥3σ, 15 of which are not present in the second MCAL GRB catalog, as they were not identified before when using traditional methods for the data analysis.…”

New detailed characterization of the residual luminescence emitted by the GAGG:Ce scintillator crystals for the HERMES Pathfinder mission

“…In [196] it is discussed a method based on CNN for classifying gamma-ray sky maps from the AGILE-GRID observatory as containing a GRB or not. The CNN processes arrays of maps, each with a size of 100 × 100 pixels, using a Convolution2D layer with 20 filters to detect features within the intensity maps.…”

“…While [222] and [196] focus on datasets free of transients (can this be verified? ), Crupi et al [67] (the foundation for Chapters 4 and 5) introduces an alternative approach.…”

Recognition of musical beat and style and applications in interactive humanoid robot

“…The difference between RNN and the traditional NN is that RNN has the concept of timing, and the state of the next moment will be affected by the current state. Some researchers also call recurrent networks deep networks, whose depth can be shown in input, output, and time-depth (Hernandez-Olivan et al, 2021 ; Parmiggiani et al, 2021 ). The RNN structure is given in Figure 2 .…”