深度学习用于对Elettra 2.0存储环进行强大的轨道控制
Mohammad Sadegh Yazdani1, S Farhad Masoudi2, Javad Rahighi3
1Department of Physics, K.N. Toosi University of Technology, P.O. Box 15875-4416, Tehran, 15418-49611, Iran.
Scientific reports
|October 16, 2025
概括
我们开发了一个深度学习框架,用于加速器中的自动电子束轨道控制. 这种方法提高了光束的稳定性,可以应用于未来的同步仪项目.
科学领域:
- 加速器物理学的物理学
- 机器学习应用 机器学习应用
- 粒子束动力学 粒子束动力学
背景情况:
- 光源加速器的进步需要高光束质量.
- 精确控制粒子轨道对于稳定,高质量的光束至关重要.
- 需要自动轨道控制来纠正光束位置错误.
研究的目的:
- 为自动电子束轨道控制提出一个计算框架.
- 利用深度学习来预测校正器磁场强度.
- 为了提高Elettra 2.0储存环设计中的光束稳定性.
主要方法:
- 使用深层卷积神经网络来预测校正器的磁场强度.
- 转移学习被用来连续训练系统和控制器深度学习模型.
- 该方法使用对Elettra 2.0存储环网格的模拟进行了评估.
主要成果:
- 深度学习方法证明了轨道控制的有效性和效率.
- 该方法在现实世界加速器应用中显示出强大的通用性.
- 与Elettra 2.0环上的奇数值分解相比,实现了7%的更大的稳定性抑制.
结论:
- 拟议的计算框架成功实现了自动电子束轨道控制.
- 这种机器学习方法为加速器的光束稳定性提供了显著的改进.
- 该框架可适应用于其他同步机设施.
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