arXiv subjects

G. Panebianco

Publications and source records attributed to G. Panebianco.

7 recordsLinked to original sources

arXiv2026

The Array Control and Data Acquisition software of the Cherenkov Telescope Array Observatory

The Cherenkov Telescope Array Observatory (CTAO) aims to advance knowledge of the gamma-ray sky as the largest gamma-ray observatory ever built. The CTAO will be deployed at two sites, one in the Northern Hemisphere and the other in the Southern Hemisphere, containing telescopes of three sizes to cover different energy domains. Commissioning of the prototype CTAO Large-Sized Telescope (LST-1) is being finalized at the northern site, while three additional LSTs are under construction. Additional calibration and environmental monitoring instruments, such as laser imaging detection and ranging (LIDAR) systems and weather stations, will support telescope operations. The Array Control and Data Acquisition (ACADA) system serves as the central element for on-site CTAO operations. ACADA controls, supervises, and handles the data generated by the telescopes and the auxiliary instruments. It drives the efficient planning and execution of observations while managing the multi-gigabit-per-second data streams produced by each CTAO telescope. The ACADA system contains the CTAO Science Alert Generation Pipeline - a real-time data processing and analysis pipeline, dedicated to automatically generating science alert candidates as data are acquired. These science alerts, along with external alerts received from other scientific instruments, are managed by the Transients Handler (TH) component. The TH informs ACADA's Short-Term Scheduler (STS) about relevant science alerts, enabling modification of ongoing observations on sub-minute timescales. This capability for rapid response, combined with the fast slewing of CTAO telescopes, makes the Observatory an excellent instrument for studying high-impact astronomical transients.

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arXiv2026

Localization and Confidence Region Estimation of Short GRBs with the COSI BGO Shield Using a HEALPix-Based Deep Learning Approach

The Compton Spectrometer and Imager is a NASA satellite mission under development that will survey the entire sky in the 0.2-5 MeV range using a wide-field germanium detector array, surrounded on the sides and bottom by active shields (the Anticoincidence Subsystem, ACS). The ACS aims to suppress and monitor background events, as well as detect transient sources, such as Gamma-Ray Bursts (GRBs), through its onboard triggering algorithm. The data related to GRBs are sent to the ground and analyzed by an automated pipeline to localize the GRBs and share their positions with the community. In this work, we present a brief GRB localization method based on ACS data, utilizing deep learning (DL) techniques, which can estimate the 90\% confidence region, including cases where it is split into multiple areas. To address this, we developed a neural network classifier that predicts the GRB location as a probability distribution across the sky map following the HEALPix framework. The distribution can be used to compute the 90\% confidence regions. Future work will compare this DL-based localization approach with classical methods such as $\chi^2$ fitting and Maximum Likelihood Estimation.

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arXiv2025

The Real-Time Data Processor Framework for Data Handling and Analysis of High-Energy Instruments

We implemented a real-time data processor (rta-dp) framework that can be used to develop real-time analysis pipelines and data handling systems to manage high-throughput data streams with distributed applications in the context of ground and space astrophysical projects and high-energy instruments. The rta-dp is based on the ZeroMQ in-memory communication framework to receive input data, share data between distributed processes, and send or receive commands and pipeline configuration. The rta-dp framework has a flexible architecture that allows the implementation of distributed analysis systems customized to the requirements of several scenarios. The rta-dp framework also provides monitoring capabilities for the running processes and sends housekeeping, logging, alarms, and informative messages that a monitoring process can acquire. We are using the rta-dp in several contexts, such as acquiring and processing data from X-ray detectors to the data quality system of the ASTRI Project, as well as reprocessing and archiving data.

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arXiv2025

The New Architecture of the Online Observation Quality System for the ASTRI Mini-Array Project

The ASTRI Mini-Array is an international collaboration led by the Italian National Institute for Astrophysics. The project aims to construct and operate an array of nine Imaging Atmospheric Cherenkov Telescopes to study gamma-ray sources at very high energy (TeV) and perform stellar intensity interferometry observations. We describe the updated Online Observation Quality System (OOQS) software architecture. The OOQS is one of the subsystems of the Supervisory Control and Data Acquisition (SCADA) system. It aims to execute real-time data quality checks on the data acquired by the Cherenkov cameras and intensity interferometry instruments and provide feedback to both SCADA and the Operator about abnormal conditions detected. The data quality results are stored in the Quality Archive for further investigation and sent to the Operator Human Machine Interface (HMI) through Kafka.

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arXiv2024

AGILE gamma-ray detection of the exceptional GRB 221009A

Gamma-ray emission in the MeV-GeV range from explosive cosmic events is of invaluable relevance to understanding physical processes related to the formation of neutron stars and black holes. Here we report on the detection by the AGILE satellite in the MeV-GeV energy range of the remarkable long-duration gamma-ray burst GRB 221009A. The AGILE onboard detectors have good exposure to GRB 221009A during its initial crucial phases. Hard X-ray/MeV emission in the prompt phase lasted hundreds of seconds, with the brightest radiation being emitted between 200 and 300 seconds after the initial trigger. Very intense GeV gamma-ray emission is detected by AGILE in the prompt and early afterglow phase up to 10,000 seconds. Time-resolved spectral analysis shows time-variable MeV-peaked emission simultaneous with intense power-law GeV radiation that persists in the afterglow phase. The coexistence during the prompt phase of very intense MeV emission together with highly nonthermal and hardening GeV radiation is a remarkable feature of GRB 221009A. During the prompt phase, the event shows spectrally different MeV and GeV emissions that are most likely generated by physical mechanisms occurring in different locations. AGILE observations provide crucial flux and spectral gamma-ray information regarding the early phases of GRB 221009A during which emission in the TeV range was reported.

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arXiv2024

Quantum Convolutional Neural Networks for the detection of Gamma-Ray Bursts in the AGILE space mission data

Quantum computing represents a cutting-edge frontier in artificial intelligence. It makes use of hybrid quantum-classical computation which tries to leverage quantum mechanic principles that allow us to use a different approach to deep learning classification problems. The work presented here falls within the context of the AGILE space mission, launched in 2007 by the Italian Space Agency. We implement different Quantum Convolutional Neural Networks (QCNN) that analyze data acquired by the instruments onboard AGILE to detect Gamma-Ray Bursts from sky maps or light curves. We use several frameworks such as TensorFlow-Quantum, Qiskit and PennyLane to simulate a quantum computer. We achieved an accuracy of 95.1% on sky maps with QCNNs, while the classical counterpart achieved 98.8% on the same data, using however hundreds of thousands more parameters.

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arXiv2024

Deep Learning for AGILE Anticoincidence System's Background Prediction from Orbital and Attitude Parameters

AGILE is an Italian Space Agency (ASI) space mission launched in 2007 to study X-ray and gamma-ray phenomena in the energy range from $\sim$20 keV to $\sim$10 GeV. The AGILE AntiCoincidence System (ACS) detects hard-X photons in the 50 - 200 keV energy range and continuously stores each panel's count rates in the telemetry. We developed a new Deep Learning (DL) model to predict the background of the AGILE ACS top panel using the satellite's orbital and attitude parameters. This model aims to learn how the orbital and spinning modulations of the satellite impact the background level of the ACS top panel. The DL model executes a regression problem, and is trained with a supervised learning technique on a dataset larger than twenty million orbital parameters' configurations. Using a test dataset, we evaluated the trained model by comparison of the predicted count rates with the real ones. The results show that the model can reconstruct the background count rates of the ACS top panel with an accuracy of 96.7\%, considering the orbital modulation and spinning of the satellite. Starting from these promising results, we are developing an anomaly detection method to detect Gamma-ray Bursts when the differences between predicted and real count rates exceed a predefined threshold.

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