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English(EN) Machine-learning-based neutrino-event generation constrained by experimental measurements

基于机器学习的 the neutrino events calibrated with experimental data

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种基于机器学习的 the neutrino-nucleus scattering events 生成模型。该模型即使在不需要 event-level data 的情况下，也可以使用 the differential cross sections 的实验测量进行校准。通过将模型微调到 MicroBooNE experiment 的特定测量，研究人员证明了对未包含在校准中的可观测量的准确性有所提高。校准后的模型在不同的 neutrino fluxes 和 beam configurations 下也证明是有效的，并保持了对 outgoing muon kinematics 的准确预测。 AI

影响 这项研究展示了机器学习在科学模拟和校准方面的新颖应用，有望提高物理实验的效率和准确性。

排序理由 该集群包含一篇 arXiv 预印本，详细介绍了物理学中的新研究方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

基于机器学习的 the neutrino events calibrated with experimental data

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该集群包含一篇 arXiv 预印本，详细介绍了物理学中的新研究方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jose Luis Bonilla, Krzysztof M. Graczyk, Rwik Dharmapal Banerjee, Beata E. Kowal, Hemant Prasad, Jan T. Sobczyk ·

    基于机器学习的 the neutrino-event generation 受实验测量约束

    arXiv:2610.20870v1 Announce Type: cross Abstract: We present a generative surrogate for charged-current \(\nu_\mu\)-nucleus scattering that can be calibrated to measured differential cross sections without event-level experimental samples. Starting from a theoretical interaction …

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