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Dipartimento di Ingegneria "Enzo Ferrari"

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2022 - The RTApipe framework for the gamma-ray real-time analysis software development [Articolo su rivista]
Parmiggiani, N.; Bulgarelli, A.; Beneventano, D.; Fioretti, V.; Di Piano, A.; Baroncelli, L.; Addis, A.; Tavani, M.; Pittori, C.; Oya, I.

In the multi-messenger era, coordinating observations between astronomical facilities is mandatory to study transient phenomena (e.g. Gamma-ray bursts) and is achieved by sharing information with the scientific community through networks such as the Gamma-ray Coordinates Network. The facilities usually develop real-time scientific analysis pipelines to detect transient events, alert the astrophysical community, and speed up the reaction time of science alerts received from other observatories. We present in this work the RTApipe framework, designed to facilitate the development of real-time scientific analysis pipelines for present and future gamma-ray observatories. This framework provides pipeline architecture and automatisms, allowing the researchers to focus on the scientific aspects and integrate existing science tools developed with different technologies. The pipelines automatically execute all the configured analyses during the data acquisition. This framework can be interfaced with science alerts networks to perform follow-up analysis of transient events shared by other facilities. The analyses are performed in parallel and can be prioritised. The workload is highly scalable on a cluster of machines. The framework provides the required services using containerisation technology for easy deployment. We present the RTA pipelines developed for the AGILE space mission and the prototype of the SAG system for the ground-based future Cherenkov Telescope Array observatory confirming that the RTApipe framework can be used to successfully develop pipelines for the gamma-ray observatories, both space and ground-based.

2021 - A Deep Learning Method for AGILE-GRID Gamma-Ray Burst Detection [Articolo su rivista]
Parmiggiani, N.; Bulgarelli, A.; Fioretti, V.; Di Piano, A.; Giuliani, A.; Longo, F.; Verrecchia, F.; Tavani, M.; Beneventano, D.; Macaluso, A.

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.