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Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment
arXiv:2605.31391v1 Announce Type: cross Abstract: Modern machine learning techniques have become increasingly important in particle physics because of their powerful pattern-recognition capabilities, including in real-time data acquisition where stringent runtime constraints apply. This paper details the performance of deep-learning-based trigger algorithms for a large water Cherenkov detector such as Hyper-Kamiokande aimed at low-energy neutrino events (below 7 MeV). The performance of...
Distant blazar OP 313 emits very high-energy gamma rays above 100 GeV
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Performance characterisation of the Hamamatsu R760 photomultiplier tube for the PLUME detector
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