Application of machine learning in orbital correction of storage ring

Li Ruichun; Zhang Qinglei; Mi Qingru; Jiang Bocheng; Wang Kun; Li Changliang; Zhao Zhentang

doi:10.11884/HPLPB202133.200318

Volume 33 Issue 3

Mar. 2021

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Article Navigation > High Power Laser and Particle Beams > 2021 > 33(3): 034007

Yu Hailong, Wu Wenzhi. Temperature-dependent photoluminescence of CH3NH3PbBr3 crystal powder[J]. High Power Laser and Particle Beams, 2023, 35: 119001. doi: 10.11884/HPLPB202335.230103

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Li Ruichun, Zhang Qinglei, Mi Qingru, et al. Application of machine learning in orbital correction of storage ring[J]. High Power Laser and Particle Beams, 2021, 33: 034007. doi: 10.11884/HPLPB202133.200318

Yu Hailong, Wu Wenzhi. Temperature-dependent photoluminescence of CH3NH3PbBr3 crystal powder[J]. High Power Laser and Particle Beams, 2023, 35: 119001. doi: 10.11884/HPLPB202335.230103

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Application of machine learning in orbital correction of storage ring

doi: 10.11884/HPLPB202133.200318

Li Ruichun^{1, 2, 3
,},
Zhang Qinglei^{4
,
,},
Mi Qingru⁴,
Jiang Bocheng⁴,
Wang Kun⁴,
Li Changliang^{1, 3, 4},
Zhao Zhentang^{1, 2, 3, 4}

1.
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
2.
ShanghaiTech University, Shanghai 201204, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China
4.
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China

Received Date: 2020-11-23
Rev Recd Date: 2021-01-19

Available Online: 2021-03-30

Publish Date: 2021-03-05

Abstract

Abstract

Synchrotron light source is one of the most powerful tools in modern science and technology. Shanghai Synchrotron Radiation Facility (SSRF), located in Shanghai, China, is an advanced 3.5 GeV 3rd-generation medium energy light source. The 3rd-generation synchrotron radiation light source will provide high brilliance and high stability synchrotron radiation to fulfill the advanced experimental conditions in frontier researches. To achieve highly stable radiation, it is important to have highly stable beam orbit. Thus we adopted machine learning method to control and feedback the orbit. Using this neural network-based orbit correction method, which doesn’t rely on the response matrix, we can establish a nonlinear mapping relationship between correctors and the orbit distortions and perform continuous online retraining. This new method can significantly improve the orbit stability of SSRF.
- Shanghai Synchrotron Radiation Facility,
- storage ring,
- orbit correction,
- orbit feedback,
- machine learning

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References(26)

References

[1]	Jiang Bocheng, Liu Guimin, Zhao Zhentang. Simulation of a transverse feedback system for the SSRF storage ring[J]. High Energy Physics and Nuclear Physics, 2007, 31(10): 956-961.
[2]	Jiang Bocheng, Lin Guoqiang, Wang Baoliang, et al. Multi-bunch injection for SSRF storage ring[J]. Nuclear Science and Techniques, 2015, 26: 050101.
[3]	Zhang Q, Jiang B C, Tian S Q, et al. Study on beam dynamics of a Knot-APPLE undulator proposed for SSRF[C]//Proceedings of the 6th International Particle Accelerator Conference. 2015: 1669-1671.
[4]	Jiang Bocheng, Zhao Zhentang, Liu Guimin. Study of Touschek lifetime in SSRF storage ring[J]. High Energy Physics and Nuclear Physics, 2006, 30(7): 693-698.
[5]	Jiang Bocheng, Xia Guoxing, Han Lifeng, et al. Investigation of fast ion instability in SSRF[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2010, 614(3): 331-334.
[6]	卜令山, 赵振堂, 殷立新, 等. 第三代同步辐射光源储存环支撑组件振动控制研究[J]. 中国物理C, 2008, 32(s1):37-39. (Bu Lingshan, Zhao Zhentang, Yin Lixin, et al. Vibration control research for the 3rd generation synchrotron light source storage ring mechanical components[J]. Chinese Physics C, 2008, 32(s1): 37-39
[7]	Zhao Z T, Xu H J, Ding H. Commissioning of the Shanghai Light Source[J]. Energy, 2015, 3: 3-51.
[8]	Nagaoka R, Bocchetta C J, Iazzourene F, et al. Orbit correction in ELETTRA[C]//Proc 4th EPAC. 1994: 1009.
[9]	Tsai HJ, Chang H P, Chou P J, et al. Closed orbit correction of TPS storage ring[C]//Proceedings of EPAC 2006.2006: 2029-2031.
[10]	Li Jingyi, Liu Gongfa, Li Weimin, et al. Closed orbit correction of HLS storage ring[C]//Proceedings of the 2001 Particle Accelerator Conference. Chicago: IEEE, 2001: 1255-1257.
[11]	Wang Faya, Song Minghao, Edelen A, et al. Machine learning for design optimization of storage ring nonlinear dynamics[DB/OL]. arXiv preprint arXiv: 1910.14220, 2019.
[12]	Leemann S C, Liu S, Hexemer A, et al. Demonstration of machine learning-based model-independent stabilization of source properties in synchrotron light sources[J]. Physical Review Letters, 2019, 123: 194801. doi: 10.1103/PhysRevLett.123.194801
[13]	刘祖平. 同步辐射光源物理引论[M]. 合肥: 中国科学技术大学出版社, 2009: 161-196. Liu Zhuping. Introduction to physics of synchrotron radiation source[M]. Hefei: University of Science and Technology of China Press, 2009: 161-196).
[14]	Chung Y, Decker G, Evans K, et al. Global DC closed orbit correction experiments on the NSLS X-ray ring and SPEAR[C]//Proceedings of International Conference on Particle Accelerators. Washington: IEEE, 1993: 2275-2277.
[15]	LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539
[16]	陈亚秋, 陈德钊, 胡上序, 等. 多层前传神经网的广义误差反传训练与模式分类[J]. 模式识别与人工智能, 1996, 9(2):161-165. (Chen Yaqiu, Chen Dezhao, Hu Shangxu, et al. Generalized error back-propagation training for multi-layered feedforward neural nets[J]. Pattern Recognition and Artificial Intelligence, 1996, 9(2): 161-165
[17]	LeCun Y, Boser B, Denker J S, et al. Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4): 541-551. doi: 10.1162/neco.1989.1.4.541
[18]	Ruder S. An overview of gradient descent optimization algorithms[J]. arXiv preprint arXiv: 1609.04747, 2016.
[19]	Chollet F, Others. Keras: the python deep learning library[J]. Astrophysics Source Code Library, 2018, 1806: 1022.
[20]	Abadi M, Barham P, Chen Jianmin, et al. TensorFlow: a system for large-scale machine learning[C]//Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation. 2016: 265-283.
[21]	Dean J, Corrado G S, Monga R, et al. Large scale distributed deep networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012: 1223-1231.
[22]	Zhang Chiyuan, Liao Qianli, Rakhlin A, et al. Theory of deep learning IIb: optimization properties of SGD[DB/OL]. arXiv preprint arXiv: 1801.02254, 2018.
[23]	Bottou L, Curtis F E, Nocedal J. Optimization methods for large-scale machine learning[J]. SIAM Review, 2016, 60(2): 223-311.
[24]	Nair V, Hinton G E. Rectified linear units improve restricted Boltzmann machines[C]//Proceedings of the 27th International Conference on Machine Learning. 2010: 807-814.
[25]	Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks[J]. Journal of Machine Learning Research, 2011, 15: 315-323.
[26]	Nowlan S J, Hinton G E. Simplifying neural networks by soft weight-sharing[J]. Neural Computation, 1992, 4(4): 473-493. doi: 10.1162/neco.1992.4.4.473

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