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.

A Deep Learning Method for AGILE-GRID Gamma-Ray Burst Detection / Parmiggiani, N.; Bulgarelli, A.; Fioretti, V.; Di Piano, A.; Giuliani, A.; Longo, F.; Verrecchia, F.; Tavani, M.; Beneventano, D.; Macaluso, A.. - In: THE ASTROPHYSICAL JOURNAL. - ISSN 0004-637X. - 914:1(2021), pp. 67-68. [10.3847/1538-4357/abfa15]

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

Parmiggiani N.;Di Piano A.;Beneventano D.;
2021

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.
914
1
67
68
A Deep Learning Method for AGILE-GRID Gamma-Ray Burst Detection / Parmiggiani, N.; Bulgarelli, A.; Fioretti, V.; Di Piano, A.; Giuliani, A.; Longo, F.; Verrecchia, F.; Tavani, M.; Beneventano, D.; Macaluso, A.. - In: THE ASTROPHYSICAL JOURNAL. - ISSN 0004-637X. - 914:1(2021), pp. 67-68. [10.3847/1538-4357/abfa15]
Parmiggiani, N.; Bulgarelli, A.; Fioretti, V.; Di Piano, A.; Giuliani, A.; Longo, F.; Verrecchia, F.; Tavani, M.; Beneventano, D.; Macaluso, A.
File in questo prodotto:
File Dimensione Formato  
Parmiggiani_2021_ApJ_914_67.pdf

accesso aperto

Tipologia: Versione dell'editore (versione pubblicata)
Dimensione 1.86 MB
Formato Adobe PDF
1.86 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1249085
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 2
social impact