机器学习在大型粒子加速器中的应用回顾与展望

万金宇; 孙正; 张相; 白宇; 蔡承颖; 储中明; 黄森林; 焦毅; 冷用斌; 李标斌; 李京祎; 李楠; 卢晓含; 孟才; 彭月梅; 王生; 张成艺

doi:10.11884/HPLPB202133.210199

机器学习在大型粒子加速器中的应用回顾与展望

doi: 10.11884/HPLPB202133.210199

万金宇^{1, 2,},
孙正^{1, 2},
张相^{1, 2},
白宇^{1, 2},
蔡承颖³,
储中明⁴,
黄森林⁵,
焦毅^{1, 2, ,},
冷用斌^{2, 6},
李标斌^{1, 2, 7},
李京祎^{1, 2},
李楠^{1, 2},
卢晓含^{1, 2, 7},
孟才^{1, 2},
彭月梅^{1, 2},
王生^{1, 2, 7},
张成艺^{1, 2}

1.
中国科学院高能物理研究所，北京 100049
2.
中国科学院大学，北京 100049
3.
华中科技大学电气与电子工程学院，武汉 430074
4.
南京大学现代工程与应用科学学院，南京 210023
5.
北京大学重离子物理研究所&核物理与核技术国家重点实验室，北京 100871
6.
中国科学院上海高等研究院，上海 201204
7.
散裂中子源科学中心，广东东莞 523803

基金项目: 国家自然科学基金项目（11922512）；中国科学院青年创新促进会项目（Y201904）；国家重点研发计划项目（2016YFA0401900）

详细信息

作者简介:
万金宇，wanjinyu@ihep.ac.cn

通讯作者:
焦　毅，jiaoyi@ihep.ac.cn

中图分类号: TL5
计量
- 文章访问数: 2115
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- PDF下载量: 355
- 被引次数: 0
出版历程
- 收稿日期: 2021-05-25
- 修回日期: 2021-08-31
- 网络出版日期: 2021-09-10
- 刊出日期: 2021-09-15

Machine learning applications in large particle accelerator facilities: review and prospects

Wan Jinyu^{1, 2
,},
Sun Zheng^{1, 2},
Zhang Xiang^{1, 2},
Bai Yu^{1, 2},
Tsai Chengying³,
Chu Paul⁴,
Huang Senlin⁵,
Jiao Yi^{1, 2
, ,},
Leng Yongbin^{2, 6},
Li Biaobin^{1, 2, 7},
Li Jingyi^{1, 2},
Li Nan^{1, 2},
Lu Xiaohan^{1, 2, 7},
Meng Cai^{1, 2},
Peng Yuemei^{1, 2},
Wang Sheng^{1, 2, 7},
Zhang Chengyi^{1, 2}

1.
Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
4.
College of Engineering and Applied Science, Nanjing University, Nanjing 210023, China
5.
Institute of Heavy Ion Physics & State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China
6.
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
7.
China Spallation Neutron Source, Dongguan Guangdong 523803, China

摘要

摘要:
机器学习技术在近十几年发展迅猛，并被广泛地用于解决复杂的科学和工程问题。最近十年间，基于机器学习的粒子加速器相关研究也开始呈现出井喷式发展趋势。国际上许多加速器实验室开始尝试用机器学习和大数据技术处理加速器中的海量复杂数据，以期解决加速器及其子系统中的诸多物理和技术问题。不过，迄今为止，机器学习在加速器中的应用仍处于初步探索阶段，不同机器学习算法在解决具体加速器问题的效果及其适用范围尚待摸索，机器学习在实际加速器中的应用仍非常有限。因此，有必要对加速器领域中的机器学习研究做一个整体回顾和总结。将回顾机器学习在大型粒子加速器（以储存环加速器和直线加速器为主）中的加速器技术、束流物理以及加速器整体性能优化等研究方向中已取得的研究成果，并探讨机器学习在加速器领域的未来发展方向和应用前景。
- 机器学习 /
- 粒子加速器 /
- 大科学装置 /
- 大数据 /
- 加速器技术 /
- 束流物理
Abstract:
Rapid growth of machine learning techniques has arisen over last decades, which results in wide applications of machine learning for solving various complex problems in science and engineering. In the last decade, machine learning and big data techniques have been widely applied to the domain of particle accelerators and a growing number of results have been reported. Several particle accelerator laboratories around the world have been starting to explore the potential of machine learning the processing the massive data of accelerators and to tried to solve complex practical problems in accelerators with the aids of machine learning. Nevertheless, current exploration of machine learning application in accelerators is still in a preliminary stage. The effectiveness and limitations of different machine learning algorithms in solving different accelerator problems have not been thoroughly investigated, which limits the further applications of machine learning in actual accelerators. Therefore, it is necessary to review and summarize the developments of machine learning so far in the accelerator field. This paper mainly reviews the successful applications of machine learning in large accelerator facilities, covering the research areas of accelerator technology, beam physics, and accelerator performance optimization, and discusses the future developments and possible applications of machine learning in the accelerator field.
- machine learning /
- particle accelerator /
- large scientific facilities /
- big data /
- accelerator technology /
- beam physics

HTML全文

图 1 有监督学习示意图

Figure 1. Schematic diagram of supervised learning

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图 2 人工神经网络神经网络拓扑结构示意图

Figure 2. Schematic diagram of artificial neural network topology

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图 3 强化学习示意图

Figure 3. Schematic diagram of reinforcement learning

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图 4 用不同的优化算法对HEPS的动力学孔径和托歇克寿命进行优化的示意图^[63]

Figure 4. Optimization of dynamic aperture and Touschek lifetime of the HEPS with multiple optimization methods^[63]

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图 5 电子枪温度设定值调整1 ℃，对应的水温变化曲线^[79]

Figure 5. Water temperature change under 1 °C step change of electron gun setting^[79]

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图 6 用神经网络增强的极值搜索控制方法对LCLS的束流纵向相空间操纵的实验^[123]

Figure 6. Longitudinal phase space manipulation experiments in LCLS with a neural network enhanced extremum seeking control method^[123]

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