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

• URL: http://arxiv.org/abs/2605.31391v1
• Date: Fri, 29 May 2026 14:56:04 GMT
• Title: Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment
• Abstract summary: This paper details the performance of deep-learning-based trigger algorithms for a large water Cherenkov detector.<n>The performance of custom neural-network supervised classifiers is shown alongside two anomaly-detection approaches trained solely on detector noise.<n>The supervised model shows signal identification efficiencies of 76.7% for single electrons of 3 MeV kinetic energy.
• Score: 0.44541388526175457
• License: http://creativecommons.org/licenses/by/4.0/
• 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 an energy-based model based on Manifold Projection--Diffusion Recovery (MPDR). 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%, as does the MPDR approach with 31.8%. Runtime evaluations on GPU yield per-window inference latencies well below the millisecond scale, indicating that real-time operation is feasible.

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