发表机构
VIT-AP University; Mohan Babu University（VIT-AP大学; 莫汉·巴布大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
利用CMS 2015年D期公开数据，在单Z\(\rightarrow\ell^+\ell^-\)末态通过提取运动学可观测量降维成特征向量，用神经样条流模拟背景和信号密度，构建检验统计量进行暗物质搜索，给出不同媒介子的信号强度参数上限。
AI 中文摘要
我们报告了一项在质心能量\(\sqrt{s}=13\) TeV下，利用CMS 2015年D期对应于2.32\(\mathrm{fb}^{-1}\)积分亮度的公开数据以及简化模型蒙特卡罗模拟，对与轻子衰变Z玻色子相关产生的暗物质（DM）进行搜索。在\(\mu\mu\)和\(ee\)通道的单Z\(\rightarrow\ell^+\ell^-\)末态中选择事件。从MINIAOD和MINIAODSIM中提取40个运动学可观测量，经物理动机选择清理后，降为37维特征向量。独立训练五个神经样条流来模拟标准模型背景和特定媒介子的DM信号密度。根据信号和背景密度估计之间的对数似然比构建每个事件的检验统计量，无需硬的横向缺失能量（MET）阈值即可在整个运动学相空间提供灵敏度。对两个通道进行同时的轮廓似然拟合，对标量媒介子得到观测（预期）95%置信水平下信号强度参数上限\(\mu<0.0177\)（0.0018），对矢量媒介子\(\mu<0.0362\)（0.0039），对轴矢量媒介子\(\mu<0.0498\)（0.0069）。由于剩余的高MET背景建模差异，观测到的上限比预期弱，而非DM信号的证据。据我们所知，这是神经样条流似然比评分首次同时在\(\mu\mu\)和\(ee\)通道中应用于使用CMS 2015年D期公开数据的单Z暗物质搜索。
英文摘要
We report a search for dark matter (DM) produced in association with a leptonically decaying \(Z\) boson at \(\sqrt{s}=13\) TeV using CMS Run 2015D open data corresponding to an integrated luminosity of \(2.32\,\mathrm{fb}^{-1}\) together with simplified-model Monte Carlo simulation. Events are selected in the mono-\(Z\rightarrow\ell^+\ell^-\) final state in both the \(μμ\) and \(ee\) channels. Forty kinematic observables are extracted from MINIAOD and MINIAODSIM, cleaned with physics-motivated selections, and reduced to a 37-dimensional feature vector. Five Neural Spline Flows are trained independently to model Standard Model background and mediator-specific DM signal densities. The per-event test statistic is constructed from the log-likelihood ratio between the signal and background density estimates, providing sensitivity across the full kinematic phase space without requiring a hard upper \(\mathrm{MET}\) threshold. A simultaneous profile-likelihood fit combining the two channels yields observed (expected) 95\% confidence level upper limits on the signal-strength parameter of \(μ<0.0177\) (\(0.0018\)) for the scalar mediator, \(μ<0.0362\) (\(0.0039\)) for the vector mediator, and \(μ<0.0498\) (\(0.0069\)) for the axial-vector mediator. The observed limits are weaker than expected because of a residual high-\(\mathrm{MET}\) background-modeling discrepancy rather than evidence for a DM signal. To our knowledge, this is the first application of Neural Spline Flow likelihood-ratio scoring to a mono-\(Z\) dark matter search using CMS Run 2015D open data simultaneously in the \(μμ\) and \(ee\) channels.
Comments29 pages, 9 figures, 17 tables. The first two authors contributed equally to this work
