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 〈E p 〉 = 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.
We report on a systematic search for hard X-ray and γ-ray emission in coincidence with fast radio bursts (FRBs) observed by the AGILE satellite. We used 13 yr of AGILE archival data searching for time coincidences between exposed FRBs and events detectable by the MCAL (0.4–100 MeV) and GRID (50 MeV–30 GeV) detectors at timescales ranging from milliseconds to days/weeks. The current AGILE sky coverage allowed us to extend the search for high-energy emission preceding and following the FRB occurrence. We considered all FRB sources currently included in catalogs and identified a subsample (15 events) for which a good AGILE exposure with either MCAL or GRID was obtained. In this paper we focus on nonrepeating FRBs, compared to a few nearby repeating sources. We did not detect significant MeV or GeV emission from any event. Our hard X-ray upper limits (ULs) in the MeV energy range were obtained for timescales from submillisecond to seconds, and in the GeV range from minutes to weeks around event times. We focus on a subset of five nonrepeating and two repeating FRB sources whose distances are most likely smaller than that of 180916.J0158+65 (150 Mpc). For these sources, our MeV ULs translate into ULs on the isotropically emitted energy of about 3 × 1046 erg, comparable to that observed in the 2004 giant flare from the Galactic magnetar SGR 1806–20. On average, these nearby FRBs emit radio pulses of energies significantly larger than the recently detected SGR 1935+2154 and are not yet associated with intense MeV flaring.
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.
Astro-rivelatore Gamma a Immagini Leggero (AGILE) is a space mission launched in 2007 to study X-ray and gamma-ray astronomy. The AGILE team developed real-time analysis pipelines to detect transient phenomena such as gamma-ray bursts (GRBs) and react to external science alerts received by other facilities. The AGILE anticoincidence system (ACS) comprises five panels surrounding the AGILE detectors to reject background-charged particles. It can also detect hard X-ray photons in the energy range 50–200 keV. The ACS data acquisition produces a time series for each panel. The time series are merged into a single multivariate time series (MTS). We present a new deep-learning model for the detection of GRBs in the ACS data using an anomaly detection technique. The model is implemented with a convolutional neural network autoencoder architecture trained in an unsupervised manner, using a data set of MTSs randomly extracted from the AGILE ACS data. The reconstruction error of the autoencoder is used as the anomaly score to classify the MTS. We calculated the associated p-value distribution, using more than 107 background-only MTSs, to define the statistical significance of the detections. We evaluate the trained model with a list of GRBs reported by the GRBWeb catalog. The results confirm the model’s capabilities to detect GRBs in the ACS data. We will implement this method in the AGILE real-time analysis pipeline.
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