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

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 custom neural-network supervised classifiers is shown alongside two anomaly-detection approaches trained solely on detector noise: a pure autoencoder and a model based on Manifold Projection-Diffusion Recovery. The supervised model shows signal identification efficiencies of 76.7% for single electrons of 3 MeV kinetic energy, significantly exceeding signal efficiencies obtained from a traditional hit-count-based trigger of 26.4%, while the Manifold Projection-Diffusion Recovery approach reaches 35.4% at the same operating point. Runtime evaluations on GPU yield per-window inference latencies well below the millisecond scale.

Instrumentation and Detectors (physics.ins-det); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex)

arXiv:2605.31391 [physics.ins-det]

(or arXiv:2605.31391v2 [physics.ins-det] for this version)

https://doi.org/10.48550/arXiv.2605.31391 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1103/72mz-x21q Focus to learn more DOI(s) linking to related resources

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