AI 中文总结
利用CMS数据，通过低多重度喷注和大缺失横动量事件，结合机器学习搜索暗物质，未发现超出标准模型的信号，并在简化模型中对暗物质产生截面和中介体质量设定了严格上限。
AI 中文摘要
本文展示了一项针对暗物质粒子的搜索，利用具有大缺失横向动量和单个包含少量成分的高能喷注的事件。该分析使用了CMS实验在质心能量为13 TeV时记录的质子-质子碰撞数据，对应2016年至2018年LHC数据采集期间记录的积分亮度为138 fb$^{-1}$。这是LHC上首次采用低多重度喷注特征的搜索。使用监督式机器学习以最大化对这一具有挑战性的最终态中潜在信号的灵敏度。未观察到超出标准模型背景预测的事件过剩。结果被用于在包含矢量和轴矢量中介体的简化模型内，在95%置信水平（CL）下设置暗物质产生截面的上限。对于质量约为150（550）GeV的暗物质粒子，中介体质量高达4250（3500）GeV在95%置信水平下被排除。
英文摘要
A search for dark matter particles is presented using events with large missing transverse momentum and a single energetic jet containing few constituents. The analysis uses proton-proton collision data recorded by the CMS experiment at a center-of-mass energy of 13 TeV. This corresponds to an integrated luminosity of 138 fb$^{-1}$, recorded during the 2016$-$2018 LHC data-taking period. This is the first search at the LHC to employ the low-multiplicity jet signature. Supervised machine learning is used to maximize sensitivity to potential signals in this challenging final state. No excess of events over the standard model background prediction is observed. The results are used to set upper limits on the dark matter production cross section at 95% confidence level (CL) within simplified models featuring vector and axial-vector mediators. For a dark matter particle mass of approximately 150 (550) GeV, mediator masses up to 4250 (3500) GeV are excluded at 95% CL.
CommentsSubmitted to Physical Review D. All figures and tables can be found at http://cms-results.web.cern.ch/cms-results/public-results/publications/SUS-23-017 (CMS Public Pages)
