Leveraging Prior Mean Models for Faster Bayesian Optimization of Particle Accelerators

TL;DR

This paper introduces a method that uses neural network-based prior models within Bayesian optimization to accelerate the tuning process of particle accelerators, demonstrating significant improvements in convergence speed even with imperfect prior information.

Contribution

The work presents a novel approach of integrating neural network-based prior mean functions into Bayesian optimization for particle accelerators, enhancing efficiency in high-dimensional tuning tasks.

Findings

Substantially faster convergence to optimal solutions in ideal cases.

Improved convergence speed even with imperfect prior models.

Successful application to real-world accelerator control scenarios.

Abstract

Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into the model used to make control decisions. In this work, we examine incorporating prior accelerator physics information into Bayesian optimization algorithms by utilizing fast executing, neural network models trained on simulated or historical datasets as prior mean functions in Gaussian process models. We show that in ideal cases, this technique substantially increases convergence speed to optimal solutions in high-dimensional tuning parameter spaces. Additionally, we demonstrate that even in non-ideal cases, where prior models of beam dynamics do not exactly match experimental conditions, the use of this technique can still enhance convergence speed. Finally, we demonstrate how these methods can be used to improve optimization in practical applications, such as transferring information gained from beam dynamics simulations to online control of the LCLS injector, and transferring knowledge gained from experimental measurements across different operating modes, such as accelerating different ion species at the ATLAS heavy ion accelerator.