用于共振搜索的连续事件加权

中文摘要 AI 辅助

我们开发了一个用于共振搜索的连续事件加权框架，该框架利用辅助事件信息，而无需将数据划分为越来越精细的灵敏度类别。硬选择是二元权重，有限类别对应于分段常数权重，在背景主导极限下，其精细划分接近连续的信号与背景密度比权重。该构造在单个物理可解释的共振谱中保留了辅助判别能力，具有可计算的统计方差，并能直接恢复所选信号产额。使用高斯、伽马和贝塔辅助观测量进行的一百万次完全显式的事件级伪实验验证了产额恢复、不确定性传播和频率覆盖，同时显著提高了发现灵敏度。该方法还进一步在公开可用的CMS Run2016G和Run2016H双光子开放数据上作为独立的真实数据基准进行测试，仅使用探测器级别的胶子融合希格斯模拟作为信号参考。这是对该框架的方法论测试，而非CMS希格斯分析。结果表明，在不增加灵敏度驱动事件类别的情况下，利用连续辅助信息是一条实用途径。

英文摘要

We develop a continuous event weighting framework for resonance searches that uses auxiliary event information without dividing the data into increasingly fine sensitivity categories. A hard selection is a binary weight, finite categories correspond to piecewise constant weights, and in the background dominated limit their fine partition approaches a continuous signal to background density ratio weight. The construction retains auxiliary discrimination in a single physically interpretable resonance spectrum, with calculable statistical variance and direct recovery of the selected signal yield. One million fully explicit event level pseudoexperiments using Gaussian, Gamma, and Beta auxiliary observables verify yield recovery, uncertainty propagation, and frequentist coverage while substantially improving discovery sensitivity. The method is further tested on publicly available CMS Run2016G and Run2016H diphoton Open Data as an independent real data benchmark, using detector level gluon fusion Higgs simulation only as the signal reference. This is a methodological test of the framework, not a CMS Higgs analysis. The results demonstrate a practical route to exploiting continuous auxiliary information without proliferating sensitivity driven event categories.

发表机构

• Institute of Physics, Academia Sinica（中央研究院物理研究所）
• Department of Physics, National Cheng Kung University（国立成功大学物理系）

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