AI 中文总结
针对探测器效率估计中标定数据稀缺导致的平滑与过拟合矛盾，提出密度引导条件神经过程，利用谱浓度控制高频特征，在MAJORANA数据上以更少训练事件实现优于现有方法的效率重建与抗过拟合性能。
AI 中文摘要
选择效率将观测计数与物理速率及稀有事件搜索极限联系起来。在标定数据稀缺的情况下，强平滑会抹平尖锐的物理变化，而高度灵活的模型则可能过拟合数据，将统计涨落误认为物理结构。我们引入了一种密度引导的条件神经过程（CNP），利用谱浓度控制高频特征可用的区域。在MAJORANA数据上，该方法在重建局部结构的同时，其测量效率在平均89%的区间内与真实效率相差不超过两倍二项式统计不确定性（两个连续谱窗口），而使用位置编码的Attentive CNP仅为37%。使用5,000个效率模型训练事件和500个上下文事件，该方法在峰核和连续谱汇总指标上均优于使用两倍事件训练的CNP和Attentive CNP，也优于仅基于上下文的核密度估计（KDE）和伯努利高斯过程（GP）。因此，我们的方法在重建尖锐物理特征与抵抗统计过拟合之间取得了平衡，并且使用更少的训练事件即可实现。
英文摘要
Selection efficiency connects observed counts to physical rates and rare-event search limits. With scarce calibration data, strong smoothing can erase sharp physical changes, while highly flexible models can overfit the data and mistake statistical fluctuations for physical structure. We introduce a density-guided conditional neural process (CNP) that uses spectral concentrations to control where high-frequency features are available. On MAJORANA data, it reconstructs local structure while matching measured efficiencies within twice their binomial statistical uncertainty in an average of 89% of bins across two continuum windows, versus 37% for Attentive CNP with positional encoding. With 5,000 efficiency-model training events and 500 context events, it exceeds CNP and Attentive CNP trained on twice as many events in both peak-core and continuum summaries, and also exceeds context-only KDE and Bernoulli GP. Our method thus balances the reconstruction of sharp physical features with resistance to statistical overfitting, and achieves this with fewer training events.
Comments10 pages, 1 figure, 7 tables, including appendices
