Machine learning enabled fast evaluation of dynamic aperture for storage ring accelerators

TL;DR

This paper introduces a machine learning approach to rapidly evaluate the dynamic aperture in storage ring accelerators, significantly reducing computation time while maintaining accuracy, and applicable across various physical models.

Contribution

A novel machine learning method for fast and accurate dynamic aperture evaluation that is model-independent and scalable to multiple design assessments.

Findings

Reduces DA evaluation computation time by about tenfold.

Maintains high accuracy comparable to long-term tracking simulations.

Applicable to diverse dynamical systems beyond storage rings.

Abstract

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.