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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

万金宇, 孙正, 张相, 等. 机器学习在大型粒子加速器中的应用回顾与展望[J]. 强激光与粒子束, 2021, 33: 094001. doi: 10.11884/HPLPB202133.210199
引用本文: 万金宇, 孙正, 张相, 等. 机器学习在大型粒子加速器中的应用回顾与展望[J]. 强激光与粒子束, 2021, 33: 094001. doi: 10.11884/HPLPB202133.210199
Wan Jinyu, Sun Zheng, Zhang Xiang, et al. Machine learning applications in large particle accelerator facilities: review and prospects[J]. High Power Laser and Particle Beams, 2021, 33: 094001. doi: 10.11884/HPLPB202133.210199
Citation: Wan Jinyu, Sun Zheng, Zhang Xiang, et al. Machine learning applications in large particle accelerator facilities: review and prospects[J]. High Power Laser and Particle Beams, 2021, 33: 094001. 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
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

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

  • 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。

  • 图  1  有监督学习示意图

    Figure  1.  Schematic diagram of supervised learning

    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

    图  3  强化学习示意图

    Figure  3.  Schematic diagram of reinforcement learning

    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]

    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]

    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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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

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