论文标题
基于模拟的LHC多循环子的仿真异常检测
Simulation-based Anomaly Detection for Multileptons at the LHC
论文作者
论文摘要
将希格斯玻色子样颗粒腐烂到多活蛋白中，是一个良好动机的过程，用于研究超出标准模型（SM）的物理。最终状态的一个独特功能是SM已知的精度。结果，模拟直接用于估计背景。当前搜索考虑特定的模型，通常专注于具有单个自由参数的模型，以简化分析和解释。在本文中，我们使用Multilepton最终状态中的机器学习探讨了信号模型不可知论搜索的最新建议。这些工具可用于同时搜索许多型号，其中一些模型在大型强子对撞机上没有专用搜索。我们发现，机器学习方法提供了跨参数空间的广泛覆盖范围，超出了当前搜索是敏感的，与专用搜索相比，必要的性能损失仅大约一个数量级。
Decays of Higgs boson-like particles into multileptons is a well-motivated process for investigating physics beyond the Standard Model (SM). A unique feature of this final state is the precision with which the SM is known. As a result, simulations are used directly to estimate the background. Current searches consider specific models and typically focus on those with a single free parameter to simplify the analysis and interpretation. In this paper, we explore recent proposals for signal model agnostic searches using machine learning in the multilepton final state. These tools can be used to simultaneously search for many models, some of which have no dedicated search at the Large Hadron Collider. We find that the machine learning methods offer broad coverage across parameter space beyond where current searches are sensitive, with a necessary loss of performance compared to dedicated searches by only about one order of magnitude.
