Frontiers of Data and Computing ›› 2019, Vol. 1 ›› Issue (2): 110-120.

doi: 10.11871/jfdc.issn.2096-742X.2019.02.010

Special Issue: “人工智能”专刊

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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
  • Supported by:
    Work supported by the Chinese Academy of Sciences, and the Chinese National Development Council.


[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