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- W4385306656 abstract "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." @default.
- W4385306656 created "2023-07-28" @default.
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- W4385306656 date "2023-07-25" @default.
- W4385306656 modified "2023-09-26" @default.
- W4385306656 title "Data reconstruction and classification with Graph neural networks in KM3NeT/ARCA" @default.
- W4385306656 doi "https://doi.org/10.22323/1.444.1194" @default.
- W4385306656 hasPublicationYear "2023" @default.
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