Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment

ArXi:2605.31391v1 Announce Type: cross 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 Cherenko detector such as Hyper-Kamiokande aimed at low-energy neutrino events (below 7 MeV