Development of novel statistical methods and application to the ATLAS experiment at the LHC

新型统计方法的开发及其在大型强子对撞机 ATLAS 实验中的应用

• 批准号: 2623725
• 金额: --
• 依托单位: Royal Holloway University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2623725
• 关键词: Development; novel; statistical; methods; application

The project concerns development of novel statistical methods for data analysis, in areas such as statistical models with uncertain error parameters, measurement of differential distributions using deconvolution (unfolding), and machine learning techniques for statistical tests, multivariate regression and density estimation. The statistical tools will be applied to physics analyses using data that will be collected by the ATLAS detector at the LHC. The physics application will involve the search for new processes in one of several possible areas, such as top-quark or Higgs boson properties. The measurements will use data from the upcoming data-taking run (run 3) with proton-proton collisions at 14 TeV. In addition, the student will contribute to the ATLAS experiment through development of statistical software for the ATLAS Collaboration as well as to the operation of the ATLAS detector at CERN.

该项目涉及开发用于数据分析的新型统计方法，涉及的领域包括具有不确定误差参数的统计模型、使用反卷积（展开）测量微分分布以及用于统计测试、多元回归和密度估计的机器学习技术。统计工具将应用于物理分析，使用的数据将收集的ATLAS探测器在大型强子对撞机。物理应用将涉及在几个可能的领域之一，如顶夸克或希格斯玻色子性质的新过程的搜索。测量将使用即将到来的数据采集运行（运行3）的数据，其中质子-质子碰撞为14 TeV。此外，学生将通过为ATLAS协作开发统计软件以及在CERN操作ATLAS探测器来为ATLAS实验做出贡献。