数据与计算发展前沿 ›› 2019, Vol. 1 ›› Issue (2): 110-120.doi: 10.11871/jfdc.issn.2096-742X.2019.02.010

所属专题: “人工智能”专刊

• 人工智能专刊 • 上一篇    下一篇

机器学习在粒子加速器的应用

储中明*(),肖邓杰,乔予思,万金宇   

  1. 中国科学院高能物理研究所,北京 100049
  • 出版日期:2019-12-20 发布日期:2020-01-15
  • 通讯作者: 储中明 E-mail:chuzm@ihep.ac.cn
  • 作者简介:Chu Zhongming was born in 1964. He received his Ph.D. in Physics from the University of Michigan, Ann Arbor in 1994. Through the SNS construction and beam commissioning stages, he was one of the core developers for the XAL software platform. During the LCLS-I commissioning he was in charge of high-level Physics model software.He served as the FRIB Controls leader responsible for budget, schedule and group management. Paul has joined IHEP as a senior research scientist under the Chinese Academy of Sciences “100 Talent Leadership Program”, and the head for the HEPS Technical Support Division and BEPC-II Controls Group leader. His research fields are accelerator physics and accelerator controls.

Machine Learning Applications for Particle Accelerators

Chu Zhongming*(),Xiao Dengjie,Qiao Yusi,Wan Jinyu   

  1. Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
  • Online:2019-12-20 Published:2020-01-15
  • Contact: Chu Zhongming E-mail:chuzm@ihep.ac.cn
  • Supported by:
    Work supported by the Chinese Academy of Sciences, and the Chinese National Development Council.

摘要:

【目的】机器学习是一个快速发展的领域,它能解决许多传统方法所无法有效解决的复杂问题。而一台现代粒子加速器如正在北京近郊建造中的超低发射度同步辐射光源,高能同步辐射光源(HEPS),则需要对数以千计的装置设备达到非常高的控制精度,才能用这台光源产出高效科研成果。本文主要为将机器学习应用于粒子加速器做一个简单介绍。【方法】对这样大型的加速器,传统控制方法可能无法满足如此复杂的运行,而本文将介绍机器学习技术可以在加速器的许多系统提供可能的帮助,并提出如何准备数据,及介绍一个适合机器学习的软件架构。【结果】一个能涵盖绝大部分加速器数据的数据库结构已经设计完成并开始开发编程。另外机器学习在加速器运行与设计上的初步应用也有了结果。【结论】机器学习在加速器的应用有很好的开始及正面的初步结果。同时,与其他单位的合作也已开展以分担工作及加速开发。随着软件架构成型及获取更多高质量数据,机器学习在加速器上应有更多很好的结果。

关键词: 机器学习, 加速器, 加速器控制, 数据库, 软件架构

Abstract:

[Objective] Machine Learning (ML) is a booming field for many complicated problems which were previously unable to solve effectively with conventional methods. In order to deliver big science findings, a modern accelerators such as the under construction low emittance synchrotron radiation based light source, High Energy Photon Source (HEPS), located in suburban Beijing require very high precession control systems to handle thousands of individual devices to work coherently and smartly to perform at its highest running level. This paper introduces some initial work for adopting ML in the accelerator field. [Method] For such a large accelerator, conventional control approach may be insufficient for handling the complexity of operations. This paper outlines that ML techniques may help the accelerator in many aspects and, more importantly, discusses how to prepare the data for ML. Also, a software architecture which is suitable for ML applications applied to accelerator field is introduced. [Results] A global database structure which can cover nearly all accelerator data has been designed and under implementation. In addition, initial works of ML for both accelerator operation and design are shown. [Conclusion] The ML techniques for accelerator start well with some positive results. In the meantime, collaborations between organizations are formed to share work load and speed up development. As the software infrastructure being developed and more good quality data being collected, the ML for accelerators should produce much better results.

Key words: machine learning, particle accelerator, accelerator control, database, software architecture