Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21670%2F24%3A00389343" target="_blank" >RIV/68407700:21670/24:00389343 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.22323/1.444.1194" target="_blank" >https://doi.org/10.22323/1.444.1194</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22323/1.444.1194" target="_blank" >10.22323/1.444.1194</a>
Alternative languages
Result language
angličtina
Original language name
Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8
Original language description
KM3NeT is a research infrastructure hosting two large-volume Cherenkov neutrino detectors which are currently under construction in the Mediterranean Sea. The KM3NeT/ARCA detector is optimised for the detection of high-energy neutrinos from astrophysical sources in the TeV-PeV energy range. Once completed, the detector will consist of 230 detection units. Here, we present a Deep Learning method using graph neural networks that is trained and applied to events gathered with 6 and 8 active detection units of KM3NeT/ARCA. Graph neural networks have been trained for classification and regression tasks, showing very promising performances in a range of different tasks like neutrino-background identification, neutrino event topology classification, energy and direction reconstruction, and also in the study of properties of muon bundles.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10308 - Astronomy (including astrophysics,space science)
Result continuities
Project
<a href="/en/project/LM2023063" target="_blank" >LM2023063: Laboratoire Souterrain de Modane – participation of the Czech Republic</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
38th International Cosmic Ray Conference (ICRC2023)
ISBN
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ISSN
1824-8039
e-ISSN
1824-8039
Number of pages
10
Pages from-to
1-10
Publisher name
PoS - Proceedings of Science, Sissa Medialab srl
Place of publication
Trieste
Event location
Nagoya
Event date
Jul 26, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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