Machine-learning in Secondary Emission Monitor (SEM) optimization

二次电子发射谱仪（SEMs）在世界各地的加速器设施中用于表征带电粒子束。您将从事监控器设计、实施、操作以及特别是图像分析的优化工作。您的工作将包括模拟和实验中的产率研究，旨在提高绝对校准以及CERN超级质子同步加速器慢提取线中使用的SEM箔的校准稳定性。该项目的目的是开发在线校准的新策略，将SEM测量与基于光学跃迁辐射（OTR）的监测器和切伦科夫探测器进行比较，改进监测器的整体设计，包括优化电极/网格以增强二次发射，特别是通过应用机器学习技术增强数据分析。

Secondary Emission Monitors (SEMs) are used at accelerator facilities aroundthe world to characterize charged particle beams. You will work on theoptimization of monitor design, implementation, operation and in particularimage analysis.Your work will include yield studies in simulation and experiments, aiming atimproving the absolute calibration, as well as the calibration stability of SEMFoils used in the CERN Super Proton Synchrotron slow extraction lines.The aim of the project will be to develop new strategies for onlinecalibration, compare SEM measurements with Optical Transition Radiation(OTR)-based monitors and Cherenkov detectors, improve the overall monitordesign, including optimization of the electrodes/grids to enhance secondaryemission, and in particular enhance data analysis through the application ofmachine learning techniques.