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Deep learning boosts low-energy neutrino detection for Hyper-Kamiokande

By PulseAugur Editorial · [2 sources] ·

Researchers have developed deep-learning algorithms to improve the detection of low-energy neutrino events in the Hyper-Kamiokande experiment. These new methods, including supervised classifiers and anomaly detection models, significantly outperform traditional trigger systems in identifying signals. The deep learning approaches achieve high identification efficiencies for events as low as 3 MeV, with inference times fast enough for real-time operation on GPUs. AI

IMPACT Enhances scientific discovery by improving data analysis in particle physics experiments.

RANK_REASON The cluster contains an academic paper detailing new deep-learning algorithms for a scientific experiment.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Katharina Lachner, Sa\'ul Alonso-Monsalve, Benjamin Richards, Davide Sgalaberna ·

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

    arXiv:2605.31391v1 Announce Type: cross 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 appl…

  2. arXiv cs.LG TIER_1 English(EN) · Davide Sgalaberna ·

    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-lear…

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