2025/12/17 by Rodriguez-Llorente, Guillermo, Gallardo, Galo, Navascués, Rodrigo Morant +3
Engineering · Physics and Astronomy · #Accelerator Physics (physics.acc-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Magnetic confinement fusion research #Nuclear reactor physics and engineering #Particle accelerators and beam dynamics
paper · doi:10.48550/arxiv.2512.15521
openalex publication_date 2025/12/17 · openalex created_date 2025/12/19 · openalex updated_date 2026/07/28
The development of nuclear fusion requires materials that can withstand extreme conditions. The IFMIF-DONES facility, a high-power particle accelerator, is being designed to qualify these materials. A critical testbed for its development is the MuVacAS prototype, which replicates the final segment of the accelerator beamline. Precise regulation of argon gas pressure within its ultra-high vacuum chamber is vital for this task. This work presents a fully data-driven approach for autonomous pressure control. A Deep Learning Surrogate Model, trained on real operational data, emulates the dynamics of the argon injection system. This high-fidelity digital twin then serves as a fast-simulation environment to train a Deep Reinforcement Learning agent. The results demonstrate that the agent successfully learns a control policy that maintains gas pressure within strict operational limits despite dynamic disturbances. This approach marks a significant step toward the intelligent, autonomous control systems required for the demanding next-generation particle accelerator facilities.