在质心系能量√s=13 TeV的质子-质子碰撞中寻找富含轻子的半可见喷注的共振产生
Search for resonant production of lepton-enriched semivisible jets in proton-proton collisions at $\sqrt{s}$ = 13 TeV
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中文总结 AI 辅助
该研究利用CMS探测器采集的138 fb⁻¹质子-质子碰撞数据，搜寻暗 sector 中SVJ的共振产生，采用双机器学习策略，在95%置信水平下对两类SVJ场景的Z'中介玻色子质量给出首个实验约束。
中文摘要 AI 辅助
本研究利用欧洲核子研究中心（CERN）大型强子对撞机（LHC）的CMS探测器采集的质心系能量√s=13 TeV、积分亮度为138 fb⁻¹的质子-质子碰撞数据，针对强耦合暗 sector 中富含轻子的半可见喷注（SVJ）的共振产生开展搜寻。研究考察两种场景：所有轻子味都富集的喷注（SVJℓ信号）和主要富集τ轻子的喷注（SVJτ信号）。分析聚焦于缺失横动量与包含非孤立轻子的喷注对准的末态，采用双机器学习策略：用图神经网络进行喷注识别，用全连接神经网络结合喷注级和事例级信息，以提升信号灵敏度与本底估计精度。信号模型假设存在重Z'中介玻色子，其与标准模型夸克的基准耦合为0.25，且不稳定暗强子发生即时衰变。在SVJℓ场景下，95%置信水平下排除中介玻色子质量最高达4.7 TeV，在SVJτ场景下排除质量介于1.8至3.5 TeV之间的中介玻色子。这些结果为富含轻子的半可见喷注提供了首个实验约束。
英文摘要
This search targets the resonant production of lepton-enriched semivisible jets (SVJs) from a strongly coupled dark sector, using 138 fb$^{-1}$ of proton-proton collision data collected with the CMS detector at the CERN LHC at $\sqrt{s}$ = 13 TeV. Two scenarios are investigated: jets enriched in all lepton flavors (SVJ$\ell$ signature) and jets predominantly enriched in tau leptons (SVJ$τ$ signature). The analysis focuses on final states in which the missing transverse momentum is aligned with jets containing nonisolated leptons. A dual machine-learning strategy is employed, using a graph neural network for jet identification and a fully connected neural network that combines jet- and event-level information to enhance signal sensitivity and background estimation. The signal models assume a heavy Z' mediator with a benchmark coupling of 0.25 to standard model quarks, together with prompt decays of unstable dark hadrons. In the SVJ$\ell$ scenario, mediator masses up to 4.7 TeV are excluded at 95% confidence level, while masses between 1.8 and 3.5 TeV are excluded in the SVJ$τ$ scenario. These results provide the first experimental constraints on lepton-enriched semivisible jets.
发表机构
- EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)（欧洲核子研究中心（CERN））
机构由 AI 辅助整理，请以论文原文为准。
