Machine learning application for particle accelerator optimization-a review

Abstract

Particle accelerators receive significant attention from researchers. This machine consists of various interdependent elements, so it is complex. Efficient system tuning and diagnostics are essential for utilizing accelerator technology. In addition, machine learning (ML) has been applied in several applications. ML methods such as artificial neural networks, random forest, reinforcement learning, genetic algorithm, and Bayesian optimization have been used for accelerator optimization. The optimization of particle accelerators covers their performance and efficiency. This paper reviews the application of ML techniques in optimizing particle accelerators, highlighting their importance in addressing the complexity inherent in accelerator systems and advancing accelerator science and technology.