August 1, 2025Open Access

Machine learning application for particle accelerator optimization-a review

Puntos clave

The review shows that applying machine learning techniques improves the efficiency of particle accelerators, addressing their complex nature.
Key methods like artificial neural networks and reinforcement learning have been effectively used, enhancing optimization outcomes.
This analysis assesses the application of various ML techniques, including Bayesian optimization and genetic algorithms, in particle accelerator systems.
Highlighting machine learning's impact, this work suggests significant advancements in accelerator science and technology may be possible with further optimization efforts.

Resumen

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.

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