The AFISS web platform for the correlation of high-energy transient events

In the multi-messenger era, facilities share their results with the scientific community through networks such as the General Coordinates Network to study transient phenomena (e.g., Gamma-ray bursts) and implement real-time analysis pipelines to detect transient events, reacting to science alerts received from other observatories. The fast analysis of transient events is crucial for detecting counterparts of gravitational waves and neutrino candidate events. In this context, collecting scientific results from different high-energy satellites observing the same transient event represents a key step in improving the statistical significance of the high-energy candidate events. This project aims to develop a system and a web platform to share information and scientific results of transient events between high-energy satellites with INAF participation (AGILE, FERMI, INTEGRAL and SWIFT). The AFISS platform implements the COMET VO- Event broker and provides a web portal where the users visualize the list of transient events detected by multi-messenger facilities and received through the GCN. The web portal could show, for each event, a summary of the scientific results shared by the real-time analysis pipelines and a list of time-correlated transient events. In addition, the platform is ready to receive results from participating facilities on sub-threshold events (STE) that cannot be shared with the community due to the low statistical significance. If the platform finds a time correlation between two or more STEs, it can promote them to science alerts. The web interface shows the list of STEs with possible time correlation with other STEs or science alerts. The platform notifies the users with an email when a new transient event is received.Comment: 4 pages, 4 figures, Astronomical Data Analysis Software and System XXXII (31 October-4 November 2022

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

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 sigma, 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

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.Comment: 4 pages, 2 figure, proceedings of the ADASS XXXIII (2023) conference, to appear in ASP Conference Seri

The AGILE real-time analysis pipelines in the multi-messenger era

In the multi-messenger era, space and ground-based observatories usually develop real-time analysis (RTA) pipelines to rapidly detect transient events and promptly share information with the scientific community to enable follow-up observations. These pipelines can also react to science alerts shared by other observatories through networks such as the Gamma-Ray Coordinates Network (GCN) and the Astronomer's Telegram (ATels). AGILE is a space mission launched in 2007 to study X-ray and gamma-ray phenomena. This contribution presents the technologies used to develop two types of AGILE pipelines using the RTApipe framework and an overview of the main scientific results. The first type performs automated analyses on new AGILE data to detect transient events and automatically sends AGILE notices to the GCN network. Since May 2019, this pipeline has sent more than 50 automated notices with a few minutes delay since data arrival. The second type of pipeline reacts to multi-messenger external alerts (neutrinos, gravitational waves, GRBs, and other transients) received through the GCN network and performs hundreds of analyses searching for counterparts in all AGILE instruments' data. The AGILE Team uses these pipelines to perform fast follow-up of science alerts reported by other facilities, which resulted in the publishing of several ATels and GCN circulars.Comment: 8 pages, 3 figures, Proceedings of the 37th International Cosmic Ray Conference (ICRC 2021), Berlin, German

The Agile Alert System For Gamma-Ray Transients

In recent years, a new generation of space missions offered great opportunities of discovery in high-energy astrophysics. In this article we focus on the scientific operations of the Gamma-Ray Imaging Detector (GRID) onboard the AGILE space mission. The AGILE-GRID, sensitive in the energy range of 30 MeV-30 GeV, has detected many gamma-ray transients of galactic and extragalactic origins. This work presents the AGILE innovative approach to fast gamma-ray transient detection, which is a challenging task and a crucial part of the AGILE scientific program. The goals are to describe: (1) the AGILE Gamma-Ray Alert System, (2) a new algorithm for blind search identification of transients within a short processing time, (3) the AGILE procedure for gamma-ray transient alert management, and (4) the likelihood of ratio tests that are necessary to evaluate the post-trial statistical significance of the results. Special algorithms and an optimized sequence of tasks are necessary to reach our goal. Data are automatically analyzed at every orbital downlink by an alert pipeline operating on different timescales. As proper flux thresholds are exceeded, alerts are automatically generated and sent as SMS messages to cellular telephones, e-mails, and push notifications of an application for smartphones and tablets. These alerts are crosschecked with the results of two pipelines, and a manual analysis is performed. Being a small scientific-class mission, AGILE is characterized by optimization of both scientific analysis and ground-segment resources. The system is capable of generating alerts within two to three hours of a data downlink, an unprecedented reaction time in gamma-ray astrophysics.Comment: 34 pages, 9 figures, 5 table

AGILE Observations of the LIGO-Virgo Gravitational-wave Events of the GWTC-1 Catalog

We present a comprehensive review of AGILE follow-up observations of the Gravitational Wave (GW) events and the unconfirmed marginal triggers reported in the first LIGO-Virgo (LV) Gravitational Wave Transient Catalog (GWTC-1). For seven GW events and 13 LV triggers, the associated 90% credible region was partially or fully accessible to the AGILE satellite at the T 0; for the remaining events, the localization region was not accessible to AGILE due to passages into the South Atlantic Anomaly, or complete Earth occultations (as in the case of GW170817). A systematic search for associated transients, performed on different timescales and on different time intervals about each event, led to the detection of no gamma-ray counterparts. We report AGILE MCAL upper limit fluences in the 400 keV-100 MeV energy range, evaluated in a time window of T 0 ± 50 s around each event, as well as AGILE GRID upper limit (UL) fluxes in the 30 MeV-50 GeV energy range, evaluated in a time frame of T 0 ± 950 s around each event. All ULs are estimated at different integration times and are evaluated within the portions of GW credible region accessible to AGILE at the different times under consideration. We also discuss the possibility of AGILE MCAL to trigger and detect a weak soft-spectrum burst such as GRB 170817A

The Second AGILE MCAL Gamma-Ray Burst Catalog: 13 yr of Observations

We present the results of a systematic search and analysis of GRBs detected by the Astrorivelatore Gamma ad Immagini LEggero (AGILE) MiniCALorimeter (MCAL; 0.4–100 MeV) over a time frame of 13 yr, from 2007 to 2020 November. The MCAL GRB sample consists of 503 bursts triggered by MCAL, 394 of which were fully detected onboard with high time resolution. The sample consists of about 44% short GRBs and 56% long GRBs. In addition, 109 bursts triggered partial MCAL onboard data acquisitions, providing further detections that can be used for joint analyses or triangulations. More than 90% of these GRBs were also detected by the AGILE Scientific RateMeters (RMs), providing simultaneous observations between 20 keV and 100 MeV. We performed spectral analysis of these events in the 0.4–50 MeV energy range. We could fit the time-integrated spectrum of 258 GRBs with a single power-law model, resulting in a mean photon index 〈β〉of−2.3. Among them, 43 bursts could also be fitted with a Band model, with peak energy above 400 keV, resulting in a mean low-energy photon index 〈α〉 = −0.6, a mean high-energy photon index 〈β〉 = −2.5, and a mean peak energy 〈Ep〉 = 640 keV. The AGILE MCAL GRB sample mostly consists of hard-spectrum GRBs, with a large fraction of short-duration events. We discuss properties and features of the MCAL bursts, whose detections can be used to perform joint broad-band analysis with other missions, and to provide insights on the high-energy component of the prompt emission in the tens of mega electron volt energy range.publishedVersio

AGILE Observations of GRB 220101A: A "new Year's Burst" with an Exceptionally Huge Energy Release

We report the AGILE observations of GRB 220101A, which took place at the beginning of 2022 January 1 and was recognized as one of the most energetic gamma-ray bursts (GRBs) ever detected since their discovery. The AGILE satellite acquired interesting data concerning the prompt phase of this burst, providing an overall temporal and spectral description of the event in a wide energy range, from tens of kiloelectronvolts to tens of megaelectronvolts. Dividing the prompt emission into three main intervals, we notice an interesting spectral evolution, featuring a notable hardening of the spectrum in the central part of the burst. The average fluxes encountered in the different time intervals are relatively moderate, with respect to those of other remarkable bursts, and the overall fluence exhibits a quite ordinary value among the GRBs detected by MCAL. However, GRB 220101A is the second farthest event detected by AGILE, and the burst with the highest isotropic equivalent energy of the entire MCAL GRB sample, releasing Eiso = 2.54 × 1054 erg and exhibiting an isotropic luminosity of Liso = 2.34 × 1052 erg s−1 (both in the 400 keV–10 MeV energy range). We also analyzed the first 106 s of the afterglow phase, using the publicly available Swift-XRT data, carrying out a theoretical analysis of the afterglow, based on the forward shock model. We notice that GRB 220101A is with high probability surrounded by a wind-like density medium, and that the energy carried by the initial shock shall be a fraction of the total Eiso, presumably near ∼50%.publishedVersio

The Online Observation Quality System Implementation for the ASTRI Mini-Array Project

The ASTRI Mini-Array project, led by the Italian National Institute for Astrophysics, aims to construct and operate nine Imaging Atmospheric Cherenkov Telescopes for high-energy gamma-ray source study and stellar intensity interferometry. Located at the Teide Astronomical Observatory in Tenerife, the project's software is essential for remote operation, emphasizing the need for prompt feedback on observations. This contribution introduces the Online Observation Quality System (OOQS) as part of the Supervisory Control And Data Acquisition (SCADA) software. OOQS performs real-time data quality checks on data from Cherenkov cameras and Intensity Interferometry instruments. It provides feedback to SCADA and operators, highlighting abnormal conditions and ensuring quick corrective actions for optimal observations. Results are archived for operator visualization and further analysis. The OOQS data quality pipeline prototype utilizes a distributed application with three main components to handle the maximum array data rate of 1.15 Gb/s. The first is a Kafka consumer that manages the data stream from the Array Data Acquisition System through Apache Kafka, handling the data serialization and deserialization involved in the transmission. The data stream is divided into batches of data written in files. The second component monitors new files and conducts analyses using the Slurm workload scheduler, leveraging its parallel processing capabilities and scalability. Finally, the process results are collected by the last component and stored in the Quality Archive.Comment: 4 pages, 2 figures, proceedings of the Astronomical Data Analysis Software & Systems XXXIII (ADASS 2023) conference, to appear in ASP Conference Seri