用近探测器数据训练神经网络，精准预测中微子核散射截面。
Machine Learning Neutrino-Nucleus Cross Sections
- 基于标准模型对称性，仅用近探测器数据训练神经网络模型。
- 模拟远探测器结果逼近理论极限，误差极小。
- 适合高能物理实验中需精确截面建模的研究者。
中微子-核散射截面是长基线中微子振荡实验的关键理论输入，但其精确建模仍具挑战。针对DUNE实验的简化但物理合理的玩具模型，我们证明：仅利用标准模型对称性，即可通过近探测器数据训练出高精度神经网络截面模型。结合模拟远探测器事件进行中微子振荡分析，结果表明该数据驱动方法所得振荡参数接近已知截面完美先验下的理论极限。我们进一步量化了通量形状、探测器分辨率不确定性和截面建模偏差的影响。此原理验证研究凸显未来近探测器数据集与数据驱动截面模型的巨大潜力。
原文摘要 · Abstract (English)
Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy model of the DUNE experiment, we demonstrate that an accurate neural-network model of the cross section -- leveraging only Standard-Model symmetries -- can be learned from near-detector data. We perform a neutrino oscillation analysis with simulated far-detector events, finding that oscillation analysis results enabled by our data-driven cross-section model approach the theoretical limit achievable with perfect prior knowledge of the cross section. We further quantify the effects of flux shape and detector resolution uncertainties as well as systematics from cross-section mismodeling. This proof-of-principle study highlights the potential of future neutrino near-detector datasets and data-driven cross-section models.
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