A Deep-learning Anomaly-detection Method to Identify Gamma-Ray Bursts in the Ratemeters of the AGILE Anticoincidence System

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 fac...

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Veröffentlicht in:	The Astrophysical journal Jg. 945; H. 2; S. 106 - 117
Hauptverfasser:	Parmiggiani, N., Bulgarelli, A., Ursi, A., Macaluso, A., Di Piano, A., Fioretti, V., Aboudan, A., Baroncelli, L., Addis, A., Tavani, M., Pittori, C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Philadelphia The American Astronomical Society 01.03.2023 IOP Publishing
Schlagworte:	Anomalies Artificial neural networks Astronomy Astrophysics Charged particles Convolutional neural networks Data acquisition Deep learning Gamma ray astronomy Gamma ray bursts Gamma rays Gamma-ray detectors Identification methods Modelling Neural networks Real time Space missions Time series Time series analysis
ISSN:	0004-637X, 1538-4357
Online-Zugang:	Volltext
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