Machine learning for orbit steering in the presence of nonlinearities

Area of Science:

• Particle Accelerator Physics
• Beam Dynamics and Control
• Machine Learning Applications in Science

Background:

• Circular particle accelerators depend on precise beam orbit correction to maintain the beam's trajectory near the ideal `golden orbit'.
• Traditional response matrix (RM) methods are effective for linear systems but struggle with nonlinear magnets and large beam perturbations, requiring iterative convergence.
• Nonlinearities in accelerator magnets cause variations in RM elements, reducing the efficacy of conventional beam steering techniques.

Purpose of the Study:

• To explore and evaluate a machine learning (ML)-based approach for beam orbit correction in circular particle accelerators.
• To compare the ML method against the standard RM-based technique under various operational conditions.
• To address potential limitations of ML models, such as dimensionality changes, for improved robustness.

Main Methods:

• Implementation and testing of an ML-based beam orbit correction strategy.
• Application of the ML approach to the SLS 2.0 synchrotron at the Paul Scherrer Institut.
• Comparative analysis of ML performance against the traditional response matrix method, including combined approaches.

Main Results:

• The ML-based approach demonstrates potential for efficient beam orbit steering, particularly in complex accelerator environments.
• Evaluation against the standard RM method under diverse conditions highlights the ML method's applicability and effectiveness.
• A proposed solution mitigates the impact of ML model dimensionality changes, enhancing performance consistency.

Conclusions:

• Machine learning provides a promising alternative to traditional methods for precise beam orbit correction in circular accelerators.
• The developed ML strategy offers improved robustness and efficiency for beam steering, especially in the presence of nonlinear magnetic fields.
• The findings support the adoption of ML techniques for advanced accelerator control and optimization.