Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8
- NázevTitle
- Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8
- Druh výsledkuResult type
- Příspěvek ve sborníkuProceedings paper
- AutořiAuthors
- F. Filippini, E. Androutsou, A. Domi, B. Spisso, Z. Bardačová, E. Eckerová, F. Mamedov, Y. Shitov, I. Štekl
- DOIDOI
- 10.22323/1.444.1194
- Časopis / citaceJournal / citation
- In: 38th International Cosmic Ray Conference (ICRC2023). Trieste: PoS - Proceedings of Science, Sissa Medialab srl, 2024. p. 1-10. vol. 444. ISSN 1824-8039.
- JazykLanguage
- eng
- ScopusScopus
- 2-s2.0-85212282863
- RIVRIV
- RIV/68407700:21670/24:00389343!RIV26-MSM-21670___
- ProjektProject
- LSM-CZ III - Podzemní laboratoř LSM - účast České republiky - LM2023063 (2023–2026)LSM-CZ III - Podzemní laboratoř LSM - účast České republiky - LM2023063 (2023–2026); Laboratoire Souterrain de Modane - účast ČRLaboratoire Souterrain de Modane – participation of the Czech Republic
AbstraktAbstract
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.
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.