Leveraging prior mean models for faster Bayesian optimization of particle accelerators.

Area of Science:

• Accelerator Physics
• Computational Science
• Machine Learning

Background:

• Particle accelerator tuning is complex and time-consuming.
• Optimization algorithms, like Bayesian optimization, can automate this process.
• Bayesian methods leverage prior information for efficient control decisions.

Purpose of the Study:

• To investigate incorporating prior accelerator physics information into Bayesian optimization.
• To utilize fast neural network models as prior mean functions in Gaussian processes.
• To enhance the efficiency and speed of accelerator tuning.

Main Methods:

• Developed a Bayesian optimization framework using Gaussian processes.
• Integrated fast neural network models, trained on simulated or historical data, as prior mean functions.
• Applied the technique to high-dimensional tuning parameter spaces.

Main Results:

• Achieved substantial increases in convergence speed in ideal scenarios.
• Demonstrated enhanced convergence even when prior models imperfectly matched experimental conditions.
• Showcased practical applications in accelerator control and knowledge transfer.

Conclusions:

• Integrating prior accelerator physics knowledge via neural networks significantly speeds up tuning.
• The method proves robust, enhancing convergence even with non-ideal prior models.
• This approach offers practical benefits for real-world accelerator operations and optimization.