Convexity of the longitudinal variation of third-order resonance driving terms and its application in dynamic aperture optimization

TL;DR

This paper proves the convexity of the longitudinal variation of third-order resonance driving terms in storage rings and leverages this property to develop an efficient dynamic aperture optimization method.

Contribution

It establishes the convexity of the RMS variation of third-order RDTs and introduces a new optimization approach utilizing this convexity for improved DA performance.

Findings

Convexity of the RMS variation of third-order RDTs is proven.

Optimization of DA can be approximated as a convex problem.

The proposed method accelerates DA optimization using geometric insights.

Abstract

The optimization of the dynamic aperture (DA) of a storage ring is typically a non-convex problem with multiple local optima. Recent studies showed that reducing the variation of resonance driving terms (RDTs) along the longitudinal position improves DA very effectively, as the reduction in the longitudinal variation of lower-order RDTs suppresses higher-order nonlinear terms. Therefore, minimizing the longitudinal variation of third-order RDTs is crucial for DA optimization. In this paper, we prove that the longitudinal variation of third-order RDTs, quantified using their RMS value $f_{3,\mathrm{rms}}$ at sextupole locations, is a special convex function. In the space of sextupole strengths, the iso-surfaces of $f_{3,\mathrm{rms}}$ are a series of concentric and coaxial ellipsoidal surfaces, with the central position possessing minimum $f_{3,\mathrm{rms}}$. The scanning results of a storage ring lattice show a strong consistency between the distributions of $f_{3,\mathrm{rms}}$ and DA, indicating that the optimization of DA can be regarded as a roughly approximate convex optimization problem. Based on this, a fast DA optimization method based on particle tracking is developed, where a high-quality initial population for an intelligent algorithm is generated with a Gaussian distribution based on the geometric structure of $f_{3,\mathrm{rms}}$.