Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning.

Area of Science:

• * Accelerator Physics
• * Machine Learning
• * Control Systems Engineering

Background:

• * Particle accelerator tuning is a complex optimization problem often requiring manual operator intervention.
• * Autonomous tuning using learning-based methods like Bayesian Optimization (BO) and Reinforcement Learning (RL) shows promise for improving performance and reducing tuning times.
• * Reinforcement Learning-trained Optimization (RLO) is an emerging approach within RL for developing specialized optimizers.

Purpose of the Study:

• * To conduct a comparative case study of Bayesian Optimization (BO) and Reinforcement Learning (RL) for autonomous particle accelerator tuning.
• * To analyze the practical challenges and merits of deploying these algorithms in real-world accelerator facilities.
• * To provide guidance for practitioners in selecting appropriate learning-based tuning algorithms.

Main Methods:

• * Comparative case study evaluating the performance of BO and RL algorithms.
• * Assessment of real-world deployment challenges and benefits for each method.
• * Analysis of algorithm suitability for different accelerator tuning tasks.

Main Results:

• * Both BO and RL have demonstrated successful adoption in particle accelerator tuning.
• * The study provides a nuanced analysis of the practical challenges and advantages of each approach.
• * Performance metrics and deployment considerations are detailed for practitioners.

Conclusions:

• * The findings will assist practitioners in choosing the most suitable learning-based tuning algorithm.
• * Accelerating the adoption of autonomous tuning algorithms can improve accelerator availability.
• * This research aims to push the operational limits of particle accelerators through advanced autonomous control.