基于CMS开放数据的流匹配法对强子单Z暗物质的灵敏度研究
Hadronic Mono-Z Dark Matter Sensitivity with Flow Matching on CMS Open Data
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中文总结 AI 辅助
该研究利用CMS开放数据，采用条件流匹配连续归一化流建模本底，开展强子单Z暗物质产生的灵敏度分析，得到三个简化模型基准的预期显著性，并证实额外喷注拓扑的区分能力。
中文摘要 AI 辅助
我们利用对应积分亮度为2.256382381 invfb的CMS 2015D Run HTMHT开放数据，开展强子单Z暗物质产生的预期灵敏度研究，其中1439523个事件满足强子单Z选择条件。背景采用条件流匹配连续归一化流建模，该模型在选定的HTMHT事件上训练，并在保留的验证集上评估，验证集被重新加权至全部选定样本。为缓解缺失物体特征导致的伪影、避免样本内评分偏差，我们对未定义的角特征应用哨兵插补，保留训练/验证集划分索引，并在选择工作点时要求报告的最小本底事例数为20。在评分前，对模拟信号应用信号侧离线触发代理。在此流程下，基线分析对三个简化模型基准的预期显著性分别为2.89σ、7.62σ和7.41σ。移除额外喷注运动学细节的消融研究使预期显著性降低53%至71%，表明额外喷注拓扑在强子单Z道中具有显著的区分能力。这些结果为预期灵敏度（未进行数据盲态解除）；研究的局限性和可复现性在“局限性”与“可复现性”章节讨论。
英文摘要
We present a projected sensitivity study for hadronic mono-$Z$ dark-matter production using CMS Run~2015D HTMHT open data corresponding to 2.256382381~\invfb, from which 1{,}439{,}523 events satisfy the hadronic mono-$Z$ selection. Backgrounds are modelled with a conditional flow-matching continuous normalizing flow trained on the selected HTMHT events and evaluated on a held-out validation split reweighted to the full selected population. To mitigate artifacts from missing-object features and avoid in-sample scoring bias we apply sentinel imputation for undefined angular features, persist the train/validation split indices, and enforce a minimum reported background yield of 20 events when selecting the working point. A signal-side offline trigger proxy is applied to the simulated signal before scoring. Under this procedure the baseline analysis yields expected significances of 2.89$σ$, 7.62$σ$, and 7.41$σ$ for three simplified-model benchmarks. An ablation study that removes the detailed extra-jet kinematics reduces the expected significance by 53--71\%, indicating that extra-jet topology carries substantial discriminating power in the hadronic mono-$Z$ channel. These results are projected sensitivities (no unblinding performed); the limitations and reproducibility of the study are discussed in Sections limitations and reproducibility.
发表机构
- VIT-AP University（VIT-AP大学）
- Mohan Babu University（莫汉·巴布大学）
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