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.

翻译：中微子-原子核散射截面是长基线中微子振荡实验的关键理论输入，但其可靠建模仍具挑战性。针对DUNE实验的一个简化但物理动机明确的玩具模型，我们证明仅利用标准模型对称性，即可从近探测器数据中学习出精确的神经网络截面模型。通过对模拟远探测器事件进行中微子振荡分析，发现基于数据驱动截面模型的振荡分析结果趋近于完美先验截面知识可达到的理论极限。我们进一步量化了通量谱形与探测器分辨率的不确定性，以及截面建模误差导致的系统效应。这项原理验证研究凸显了未来中微子近探测器数据集与数据驱动截面模型的潜力。