Advanced Control Methods for Particle Accelerators (ACM4PA) 2019

A. Scheinker, C. Emma, A. Edelen, S. Gessner
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引用次数: 8

Abstract

Los Alamos is currently developing novel particle accelerator controls and diagnostics algorithms to enable higher quality beams with lower beam losses than is currently possible. The purpose of this workshop was to consider tuning and optimization challenges of a wide range of particle accelerators including linear proton accelerators such as the Los Alamos Neutron Science Center (LANSCE), rings such as the Advanced Photon Source (APS) synchrotron, free electron lasers (FEL) such as the Linac Coherent Light Source (LCLS) and LCLS-II, the European X-ray Free Electron Laser (EuXFEL), the Swiss FEL, and the planned MaRIE FEL, and plasma wake-field accelerators such as FACET, FACET-II, and AWAKE at CERN. One major challenge is an the ability to quickly create very high quality, extremely intense, custom current and energy profile beams while working with limited real time non-invasive diagnostics and utilizing time-varying uncertain initial beam distributions and accelerator components. Currently, a few individual accelerator labs have been developing and applying their own diagnostics tools and custom control and ML algorithms for automated machine tuning and optimization. The goal of this workshop was to bring together a group of accelerator physicists and accelerator related control and ML experts in order to define which controls and diagnostics would be most useful for existing and future accelerators and to create a plan for developing a new family of algorithms that can be shared and maintained by the community.
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粒子加速器先进控制方法(ACM4PA) 2019
洛斯阿拉莫斯目前正在开发新的粒子加速器控制和诊断算法,以实现比目前可能的更高质量的光束和更低的光束损失。本次研讨会的目的是考虑各种粒子加速器的调谐和优化挑战,包括线性质子加速器,如洛斯阿拉莫斯中子科学中心(LANSCE),环,如先进光子源(APS)同步加速器,自由电子激光器(FEL),如直线相干光源(LCLS)和LCLS- ii,欧洲x射线自由电子激光器(EuXFEL),瑞士FEL和计划中的MaRIE FEL,以及等离子体尾流场加速器,如欧洲核子研究中心的FACET、FACET- ii和AWAKE。一个主要的挑战是,在有限的实时非侵入性诊断和利用时变不确定的初始光束分布和加速器组件的情况下,快速创建高质量、超高强度、定制电流和能量剖面光束的能力。目前,一些独立的加速器实验室一直在开发和应用他们自己的诊断工具、自定义控制和ML算法,用于自动机器调谐和优化。本次研讨会的目标是将一群加速器物理学家和加速器相关的控制和机器学习专家聚集在一起,以确定哪些控制和诊断对现有和未来的加速器最有用,并制定一个计划,开发一个可以由社区共享和维护的新算法家族。
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