Machine-learning in Secondary Emission Monitor (SEM) optimization

二次排放监测 (SEM) 优化中的机器学习

• 批准号: 2444262
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2444262
• 关键词: Machine; learning; Secondary; Emission; Monitor

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.

世界各地的加速器设施都使用二次发射监测器 (SEM) 来表征带电粒子束。您将致力于监视器设计、实施、操作，特别是图像分析的优化。您的工作将包括模拟和实验中的产量研究，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 SEMFoils 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 测量与光跃迁进行比较 基于辐射（OTR）的监视器和切伦科夫探测器改进了整体监视器设计，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。