机器学习用于在非线性存在的情况下进行轨道方向控制
Simona Bettoni1, Jonas Kallestrup1, Güney Erin Tekin1
1Paul Scherrer Institute, Center for Accelerator Science and Engineering, 5232 Villigen, Switzerland.
Journal of synchrotron radiation
|April 11, 2025
概括
机器学习为精确的圆形粒子加速器束轨道校正提供了强大的解决方案,克服了传统响应矩阵方法的局限性,特别是在非线性磁环境中. 这种方法提高了像SLS 2.0同步加速器这样的加速器的转向效率和精度.
科学领域:
- 粒子加速器物理学 物理
- 光束动力学和控制控制
- 机器学习在科学中的应用
背景情况:
- 圆形粒子加速器依赖于精确的光束轨道校正,以保持光束的轨迹靠近理想的"黄金轨道".
- 传统的响应矩阵 (RM) 方法对于线性系统是有效的,但与非线性磁铁和大束扰动作斗争,需要代收.
- 加速器磁铁的非线性导致RM元件的变化,降低了传统光束转向技术的有效性.
研究的目的:
- 探索和评估基于机器学习 (ML) 的方法,用于圆形粒子加速器中的光束轨道校正.
- 在各种操作条件下,将ML方法与基于RM的标准技术进行比较.
- 解决ML模型的潜在局限性,例如维度变化,以提高稳定性.
主要方法:
- 基于ML的光束轨道校正策略的实施和测试.
- 将ML方法应用于保罗·谢勒研究所的SLS 2.0同步仪.
- 对ML性能与传统响应矩阵方法进行比较分析,包括组合方法.
主要成果:
- 基于ML的方法证明了高效的光束轨道转向的潜力,特别是在复杂的加速器环境中.
- 在不同条件下对标准RM方法进行评估,凸显了ML方法的适用性和有效性.
- 提出的解决方案减轻了ML模型维度变化的影响,提高了性能的一致性.
结论:
- 机器学习为循环加速器中精确的光束轨道校正提供了传统方法的有希望的替代方案.
- 开发的ML策略为光束转向提供了更高的稳定性和效率,特别是在存在非线性磁场的情况下.
- 这些发现支持采用ML技术来进行先进的加速器控制和优化.
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