Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Jianhao Xu

TL;DR

This work introduces a machine-learning-based inverse-mapping framework that predicts magnet errors directly from BPM-measured optics and COD in a four-bend achromat storage-ring lattice, bypassing traditional LOCO-based inversion. By generating a large, labeled dataset with ELEGANT simulations and evaluating LR, SVM, RBFNN, and DenseNet, the study demonstrates that RBFNN and related nonlinear models can accurately recover quadrupole and sextupole errors and reconstruct ideal optics in many cases, enabling faster commissioning and online diagnostics. Correlation analyses reveal nonlinear dependencies between errors and optics, guiding feature selection and model choice, while compensation experiments show substantial reductions in beta-beating and dispersion variations, validating the practical potential of the approach for next-generation diffraction-limited rings. The work also discusses limitations and future directions, including broader error models and scalable architectures for larger facilities.

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.

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

TL;DR

This work introduces a machine-learning-based inverse-mapping framework that predicts magnet errors directly from BPM-measured optics and COD in a four-bend achromat storage-ring lattice, bypassing traditional LOCO-based inversion. By generating a large, labeled dataset with ELEGANT simulations and evaluating LR, SVM, RBFNN, and DenseNet, the study demonstrates that RBFNN and related nonlinear models can accurately recover quadrupole and sextupole errors and reconstruct ideal optics in many cases, enabling faster commissioning and online diagnostics. Correlation analyses reveal nonlinear dependencies between errors and optics, guiding feature selection and model choice, while compensation experiments show substantial reductions in beta-beating and dispersion variations, validating the practical potential of the approach for next-generation diffraction-limited rings. The work also discusses limitations and future directions, including broader error models and scalable architectures for larger facilities.

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.

Paper Structure

This paper contains 10 sections, 5 equations, 11 figures, 5 tables.

Table of Contents

  1. Introduction
  2. METHODOLOGY
  3. Prediction framework
  4. Data generation and preprocessing
  5. Data Analysis
  6. Model Selection and Hyperparameters optimization
  7. Model selection
  8. Hyperparameters optimization
  9. Results and Discussion
  10. Summary and Conclusion

Figures (11)

  • Figure 1: Error prediction framework: from physical modeling to ML-based inverse mapping.
  • Figure 2: The magnet layout and linear optics functions of one cell.
  • Figure 3: Layout of BPMs and correctors in one cell.
  • Figure 4: Top 10 most correlated features for representative targets (M1-M3 in Cell 1).
  • Figure 5: Correlation matrix of BPM-measured parameters.
  • ...and 6 more figures