Machine learning-based prediction of magnet errors in storage ring light sources

TL;DR

This paper introduces a machine learning framework that predicts magnet errors in storage rings using beam data, improving correction speed and accuracy for better beam performance.

Contribution

It presents a novel ML-based approach for directly predicting magnet errors from beam measurements, enhancing storage ring correction methods.

Findings

ML models accurately predict magnet errors

Effective reconstruction of ideal optics achieved

Potential for faster storage ring commissioning

Abstract

Magnet errors in storage rings significantly degrade beam performance, impacting the brightness and stability of the light source. Therefore, beam-based correction is crucial for the safe operation of machines and the stability of radiated photons. Unlike traditional correction methods such as linear optics from closed orbit, this paper proposes a machine learning (ML) framework to directly predict quadrupole/sextupole gradient errors and misalignment from beam position monitor-measured optics functions and closed-orbit distortion data. Based on a four-bend achromat storage ring lattice, we generate training datasets through ELEGANT numerical simulations and compare regression performance of Linear Regression, Support Vector Machine, Radial Basis Function Neural Network and Densely Connected Convolutional Network. Results demonstrate that ML models can effectively predict magnet errors and reconstruct ideal optics. This approach offers a novel strategy for accelerating storage ring commissioning and optimization, online diagnostics, and dynamic compensation for next-generation diffraction-limited rings.