用事件分类提升中微子能重建精度，降低系统误差。
Improving Neutrino Oscillation Measurements through Event Classification
- 按相互作用类型分类事件，利用不同过程的动量差异
- 在模拟DUNE实验中使能重建精度提升10%-20%
- 对微观物理模型偏差不敏感，适合未来长程中微子实验
精确的中微子能量重建对下一代长基线振荡实验至关重要，但当前方法受限于中微子-核相互作用建模的较大不确定性。已知不同相互作用通道产生不同的缺失能量，从而影响重建性能，而传统量能器方法未加以利用。本文提出在能量重建前，基于标注生成器事件的监督学习方法，对准弹性散射、介子交换电流、共振产生和深度非弹性散射等过程进行事件分类，挖掘其内在动量差异。跨生成器测试表明该方法对微观物理建模偏差具有鲁棒性；应用于模拟的DUNE ν_μ消失分析时，重建精度与灵敏度提升达10%-20%。结果表明，此策略为减少未来振荡测量中的重建驱动系统误差提供了可行路径。
原文摘要 · Abstract (English)
Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematically varying amounts of missing energy and therefore yield different reconstruction performance--information that standard calorimetric approaches do not exploit. We introduce a strategy that incorporates this structure by classifying events according to their underlying interaction type prior to energy reconstruction. Using supervised machine-learning techniques trained on labeled generator events, we leverage intrinsic kinematic differences among quasi-elastic scattering, meson-exchange current, resonance production, and deep-inelastic scattering processes. A cross-generator testing framework demonstrates that this classification approach is robust to microphysics mismodeling and, when applied to a simulated DUNE $ν_μ$ disappearance analysis, yields improved accuracy and sensitivity at the 10-20% level. These results highlight a practical path toward reducing reconstruction-driven systematics in future oscillation measurements.
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