机器学习实现了储存环加速器动态孔径的快速评估
Machine learning enabled fast evaluation of dynamic aperture for storage ring accelerators
摘要：
对于任何基于储存环的大型科学设施，最重要的性能参数之一是动态孔径（DA），它以全局方式测量带电粒子的运动稳定性。到目前为止，长期跟踪模拟被认为是最可靠的方法来计算DA。然而，数值跟踪可能会成为一个重要的问题，特别是当大量的候选设计的存储环需要进行评估。在本文中，我们提出了一种新的基于机器学习的方法，它可以减少DA跟踪的计算成本约一个数量级，同时保持足够高的评估精度。此外，我们证明了这种方法是独立的存储环的具体物理模型。这种方法具有潜在的应用于识别其他复杂动力系统中的不规则运动的类似问题。
英文摘要：
For any storage ring-based large-scale scientific facility, one of the most important performance parameters is the dynamic aperture (DA), which measures the motion stability of charged particles in a global manner. To date, long-term tracking-based simulation is regarded as the most reliable method to calculate DA. However, numerical tracking may become a significant issue, especially when lots of candidate designs of a storage ring need to be evaluated. In this paper, we present a novel machine learning-based method, which can reduce the computation cost of DA tracking by approximately one order of magnitude, while keeping sufficiently high evaluation accuracy. Moreover, we demonstrate that this method is independent of concrete physical models of a storage ring. This method has the potential to be applied to similar problems of identifying irregular motions in other complex dynamical systems.
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