Optimal strategies for testing physics beyond the Standard Model

该项目是关于开发将超越标准模型物理模型与数据进行比较的新方法，重点是多个数据集的最佳组合，并在当前统计限制的背景下设计定制的观察表，以实现最大的预期模型灵敏度。这将扩展分析-重铸研究的能力，以对新模型的参数施加限制，提出新的实验测量策略，并最大限度地重复使用大型强子对撞机数据集。学生将使用统计算法和机器学习方法，使用模拟的对撞机事件和公共数据集，并与重铸团队和LHC BSM重新解释论坛合作。

This project is about developing new methods for comparison of Beyond Standard Model physics models to data, with emphasis on optimal combination of multiple datasets, and on designing custom observables for maximum expected model sensitivity in the context of current statistical limits. This will extend the ability of analysis-recasting studies to place constraints on the parameters of new models, propose new experimental measurement strategies, and maximise reuse of the LHC dataset. The student will work with statistical algorithms and machine-learning methods, using simulated collider events and public datasets, and collaborate with recasting teams and the LHC BSM Reinterpretation Forum.