Utilizza questo identificativo per citare o creare un link a questo documento:
http://hdl.handle.net/20.500.12386/34089
Campo DC | Valore | Lingua |
---|---|---|
dc.contributor.author | PARMIGGIANI, Nicolo' | en_US |
dc.contributor.author | BULGARELLI, ANDREA | en_US |
dc.contributor.author | URSI, ALESSANDRO | en_US |
dc.contributor.author | Macaluso, A. | en_US |
dc.contributor.author | Di Piano, A. | en_US |
dc.contributor.author | FIORETTI, Valentina | en_US |
dc.contributor.author | Aboudan, A. | en_US |
dc.contributor.author | Baroncelli, L. | en_US |
dc.contributor.author | Addis, A. | en_US |
dc.contributor.author | TAVANI, Marco | en_US |
dc.contributor.author | PITTORI, Carlotta | en_US |
dc.date.accessioned | 2023-04-19T09:40:23Z | - |
dc.date.available | 2023-04-19T09:40:23Z | - |
dc.date.issued | 2023 | en_US |
dc.identifier.issn | 0004-637X | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12386/34089 | - |
dc.description.abstract | 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 anti-coincidence 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 (CNN) architecture trained in an unsupervised manner, using a dataset 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 $10^7$ 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. | en_US |
dc.language.iso | eng | en_US |
dc.title | A Deep-learning Anomaly-detection Method to Identify Gamma-Ray Bursts in the Ratemeters of the AGILE Anticoincidence System | en_US |
dc.type | Article | - |
dc.identifier.doi | 10.3847/1538-4357/acba0a | en_US |
dc.identifier.url | https://iopscience.iop.org/article/10.3847/1538-4357/acba0a | en_US |
dc.relation.medium | STAMPA | en_US |
dc.relation.volume | 945 | en_US |
dc.relation.issue | 2 | en_US |
dc.relation.firstpage | 106 | en_US |
dc.type.referee | REF_1 | en_US |
dc.description.international | no | en_US |
dc.contributor.country | ITA | en_US |
dc.relation.scientificsector | FIS/05 - ASTRONOMIA E ASTROFISICA | en_US |
dc.relation.journal | THE ASTROPHYSICAL JOURNAL | en_US |
dc.type.miur | 262 Articolo in rivista | - |
dc.description.apc | sì | en_US |
dc.description.oa | 1 – prodotto con file in versione Open Access (allegare il file al passo 5-Carica) | en_US |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
crisitem.journal.journalissn | 0004-637X | - |
crisitem.journal.ance | E016252 | - |
crisitem.author.dept | OAS Bologna | - |
crisitem.author.dept | OAS Bologna | - |
crisitem.author.dept | IAPS Roma | - |
crisitem.author.dept | OAS Bologna | - |
crisitem.author.dept | IAPS Roma | - |
crisitem.author.dept | O.A. Roma | - |
crisitem.author.orcid | 0000-0002-4535-5329 | - |
crisitem.author.orcid | 0000-0001-6347-0649 | - |
crisitem.author.orcid | 0000-0002-7253-9721 | - |
crisitem.author.orcid | 0000-0002-6082-5384 | - |
crisitem.author.orcid | 0000-0003-2893-1459 | - |
crisitem.author.orcid | 0000-0001-6661-9779 | - |
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