用神经样条流模型搜索单Z+暗物质，首次在双通道实现高维特征建模。
Mono-Z Dark Matter Search with Neural Spline Flows Using CMS Run 2015D Open Data
- 构建37维特征向量，用5个神经样条流分别建模背景与信号分布
- 在μμ和ee通道联合分析，对标量中介子的极限为μ<0.0177（预期0.0018）
- 无需强MET截断即可全相空间敏感，适合暗物质新物理探索者
我们利用CMS Run 2015D公开数据（积分亮度2.32 fb⁻¹，√s=13 TeV）开展单Z→ℓ⁺ℓ⁻末态中与轻子型衰变Z玻色子关联产生的暗物质（DM）搜寻。事件在μμ和ee通道中被选取，从MINIAOD和MINIAODSIM提取40个运动学变量，经物理动机筛选后形成37维特征向量。独立训练五个神经样条流模型，分别拟合标准模型背景及不同中介子类型的暗物质信号密度。通过信号与背景密度估计的对数似然比构造每事件检验统计量，实现全相空间敏感性，无需设定硬性MET上限。联合两个通道的联合轮廓似然拟合得到95%置信度上限：标量中介子μ<0.0177（预期0.0018），矢量中介子μ<0.0362（预期0.0039），轴矢量中介子μ<0.0498（预期0.0069）。观测限值弱于预期，源于残余高MET背景建模偏差，而非暗物质信号证据。据我们所知，这是首次将神经样条流似然比评分应用于同时覆盖μμ和ee通道的单Z暗物质搜寻。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。