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English(EN) Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment

深度学习提升Hyper-Kamiokande的低能中微子探测能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了深度学习算法，以改进Hyper-Kamiokande实验中低能中微子事件的探测。这些新方法，包括监督分类器和异常检测模型，在识别信号方面显著优于传统的触发系统。深度学习方法实现了高达3 MeV的事件的高识别效率，并且推理速度足够快，可以在GPU上进行实时操作。 AI

影响 通过改进粒子物理实验中的数据分析来增强科学发现。

排序理由 该集群包含一篇详细介绍用于科学实验的新深度学习算法的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

深度学习提升Hyper-Kamiokande的低能中微子探测能力

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该集群包含一篇详细介绍用于科学实验的新深度学习算法的学术论文。
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报道来源 [2]

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

    面向 Hyper-Kamiokande 实验的基于深度学习的低能触发算法

    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 ·

    用于 Hyper-Kamiokande 实验的基于深度学习的低能触发算法

    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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