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A machine-learning based closed orbit feedback for the SSRF storage ring | |
2022-12-02 | |
状态 | 已发表 |
摘要 | In order to improve the stability of synchrotron radiation, we developed a new method of machine learning-based closed orbit feedback and piloted it in the storage ring of the Shanghai Synchrotron Radiation Facility (SSRF). In our experiments, not only can the machine learning-based closed orbit feedback carry out horizontal, vertical and RF frequency feedback simultaneously, but it also has better convergence and convergence speed than the traditional Slow Orbit Feed Back (SOFB) system. What's more, the residual values of the correctors' currents variations after correction can be almost ignored. This machine learning-based new method is expected to establish a new closed orbit feedback system and improve the orbit stability of the storage ring in daily operation. |
DOI | arXiv:2212.01010 |
相关网址 | 查看原文 |
出处 | Arxiv |
WOS记录号 | PPRN:23871591 |
WOS类目 | Physics, Particles& Fields |
资助项目 | National Natural Science Foundation of China[ |
文献类型 | 预印本 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/348325 |
专题 | 物质科学与技术学院 物质科学与技术学院_特聘教授组_赵振堂组 物质科学与技术学院_博士生 |
作者单位 | 1.Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai Synchrotron Radiat Facil, Shanghai 201210, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China 3.Shanghai Tech Univ, Sch Phys Sci & Technol, Shanghai 201210, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Chongqing Univ, Chongqing 401331, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Ruichun,Zhang, Qinglei,Jiang, Bocheng,et al. A machine-learning based closed orbit feedback for the SSRF storage ring. 2022. |
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