Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment
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 identific...
| Authors | Katharina Lachner, Saúl Alonso-Monsalve, Benjamin Richards et al. |
|---|---|
| Primary category | physics.ins-det |
| Published | 29 May 2026 |
| Last updated at source | 15 September 2026 |
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