Real-time luminosity optimization in collider experiments using reinforcement learning

This study presents the development and implementation of a reinforcement learning-based algorithm for real-time luminosity tuning in collider experiments. The algorithm is initially pretrained on historical collider data and subsequently fine-tuned online during experiments. By analyzing accelerator measurements collected over several seconds, the model adjusts the magnetic structure to stabilize luminosity under varying experimental conditions. The proposed method allows for adaptive optimization without operator involvement, improving operational efficiency and stability. Results from its application on the VEPP-4M collider are presented, showcasing the method's feasibility and offering insights for its future development and application in accelerator systems.