{"ID":2828085,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15521","arxiv_id":"2512.15521","title":"Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models","abstract":"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.","short_abstract":"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 be...","url_abs":"https://arxiv.org/abs/2512.15521","url_pdf":"https://arxiv.org/pdf/2512.15521v1","authors":"[\"Guillermo Rodriguez-Llorente\",\"Galo Gallardo\",\"Rodrigo Morant Navascués\",\"Nikita Khvatkin Petrovsky\",\"Anderson Sabogal\",\"Roberto Gómez-Espinosa Martín\"]","published":"2025-12-17T15:19:55Z","proceeding":"physics.acc-ph","tasks":"[\"physics.acc-ph\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}