可微Preisach模型用于迟滞加速器系统的表征和优化
Differentiable Preisach Modeling for Characterization and Optimization of Accelerator Systems with Hysteresis
摘要：
粒子加速器性能的未来改进取决于加速器越来越精确的在线建模。在用于通知控制算法的在线加速器模型中，加速器的磁性，机械和材料组件中的滞后效应通常被忽略，即使在高精度加速器中，来自表现出滞后的系统的再现性误差也不可忽略。在这项工作中，我们结合了经典的Preisach滞后模型与机器学习技术，有效地创建非参数，高保真模型的任意系统表现出滞后。我们证明，我们的技术准确地预测物理加速器磁铁的磁滞效应。我们还通过实验演示了如何使用这些方法可以在原位，其中的磁滞模型与贝叶斯统计模型相结合的光束响应，允许表征的磁滞在加速器磁铁完全从测量的光束。此外，我们探讨如何使用这些联合的悬臂梁模型，使我们能够克服优化性能的限制时，滞后效应被忽略。
英文摘要：
Future improvements in particle accelerator performance is predicated on increasingly accurate online modeling of accelerators. Hysteresis effects in magnetic, mechanical, and material components of accelerators are often neglected in online accelerator models used to inform control algorithms, even though reproducibility errors from systems exhibiting hysteresis are not negligible in high precision accelerators. In this work, we combine the classical Preisach model of hysteresis with machine learning techniques to efficiently create non-parametric, high-fidelity models of arbitrary systems exhibiting hysteresis. We demonstrate that our technique accurately predicts hysteresis effects in physical accelerator magnets. We also experimentally demonstrate how these methods can be used in-situ, where the hysteresis model is combined with a Bayesian statistical model of the beam response, allowing characterization of hysteresis in accelerator magnets solely from measurements of the beam. Furthermore, we explore how using these joint hysteresis-beam models allows us to overcome optimization performance limitations when hysteresis effects are ignored.
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