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arXiv Machine Learning By Katharina Lachner, Sa\'ul Alonso-Monsalve, Benjamin Richards, Davide Sgalaberna

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

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The paper presents deep‑learning trigger algorithms for the Hyper‑Kamiokande water Cherenkov detector, targeting low‑energy neutrino events below 7 MeV. It compares a supervised neural‑network classifier with two anomaly‑detection methods—an autoencoder and a Manifold Projection‑Diffusion Recovery model—showing the supervised model achieves a 76.7 % signal efficiency for 3 MeV electrons, far surpassing the 26.4 % efficiency of a traditional hit‑count trigger. GPU‑based runtime tests indicate per‑window inference latencies well below one millisecond.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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