CMS ECAL Upgrade and BSM Physics

在这个学生项目中，您将开发和测试改进的电子光子触发算法，以利用HL LHC 2029的CMS晶体电磁量能器ECAL升级所提供的重大改进。这次升级将为1级触发器提供更强的空间和定时信息，并大大改善对ECAL光电探测器中异常信号的抑制，否则这些信号会产生不可接受的高触发率。高性能FPGA的使用将允许使用高级综合HLS开发更先进的算法，该算法将充分利用这种更高分辨率的数据来提高CMS在第2阶段的触发能力。您将参与HLS固件和C仿真器算法的开发，CERN的测试束参与以及相关的数据分析。从2026年开始，您还将有机会参加CERN新安装的ECAL探测器和探测器外电子设备的测试和调试。LHC Run 3将为物理学提供丰富的数据集，比迄今为止提供的综合光度增加一倍以上。您将为充分利用这些数据的领先物理分析做出重大贡献。我们的团队对超越标准模型的BSM模型有着长期的兴趣，例如NMSSM，它扩展了希格斯部门，包括轻希格斯玻色子。您将参与分析最终状态下针对轻希格斯玻色子的事件，并将充分利用最新的机器学习方法开发最先进的重建技术。

In this studentship you will develop and test improved electron photon trigger algorithms to harness the significant improvements offered by the upgrade of the CMS Crystal Electromagnetic calorimeter ECAL for HL LHC 2029. The upgrade will provide enhanced spatial and timing information to the Level 1 trigger and a greatly improved rejection of anomalous signals in the ECAL photodetectors that would otherwise generate unacceptably high triggering rates. The use of high performance FPGAs will allow more advanced algorithms to be developed using High Level Synthesis HLS that will take full advantage of this higher resolution data to improve the triggering capability of CMS in Phase 2. You will be involved in HLS firmware and C emulator algorithm developments, test beam participation at CERN and the associated data analysis. You will also have the possibility to take part in the testing and commissioning of the newly installed ECAL on detector and off detector electronics at CERN starting from 2026. LHC Run 3 will provide a rich dataset for Physics, more than doubling the integrated luminosity that has been delivered up to now. You will make a significant contribution to a leading physics analysis that fully exploits these data. Our groups have a long standing interest in Beyond the Standard Model BSM models, such as the NMSSM, which have extended Higgs sectors including light Higgs bosons. You will participate in the analysis of events targeting light Higgs bosons in the final state and will develop state of the art reconstruction techniques making full use of the latest machine learning methods.