This position paper presents a vision for self-driving particle accelerators that operate autonomously with minimal human intervention. We propose that future facilities be designed through artificial intelligence (AI) co-design, where AI jointly optimizes the accelerator lattice, diagnostics, and science application from inception to maximize performance while enabling autonomous operation. Rather than retrofitting AI onto human-centric systems, we envision facilities designed from the ground up as AI-native platforms. We outline nine critical research thrusts spanning agentic control architectures, knowledge integration, adaptive learning, digital twins, health monitoring, safety frameworks, modular hardware design, multimodal data fusion, and cross-domain collaboration. This roadmap aims to guide the accelerator community toward a future where AI-driven design and operation deliver unprecedented science output and reliability.
Apple（苹果公司）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
We describe an approach to learning optimal control policies for a large, linear particle accelerator using deep reinforcement learning coupled with a high-fidelity physics engine. The framework consists of an AI controller that uses deep neural nets for state and action-space representation and learns optimal policies using reward signals that are provided by the physics simulator. For this work, we only focus on controlling a small section of the entire accelerator. Nevertheless, initial results indicate that we can achieve better-than-human level performance in terms of particle beam curren…
6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence
Department of Computer and Network Engineering, United Arab Emirates University, UAE（计算机与网络工程系，阿联酋大学）；Research Institute for Digital Future, Khalifa University, UAE（数字未来研究院，哈利法大学）
Brilliance: toward autonomous computational research in particle accelerator physics
Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；China Spallation Neutron Source（中国散裂中子源）
Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is of-ten the first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information is known about the beam response is scarce. In this work, we describe an adaptation of the popular Bayes…
Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models
HI Iberia（HI伊比利亚）；Department of Mathematics, Universidad Carlos III de Madrid（数学系，卡洛斯三世大学）；Gregorio Millán Barbany Institute, Universidad Carlos III de Madrid（格里戈里·米兰·巴尔巴尼研究所，卡洛斯三世大学）；IFMIF-DONES Spain（IFMIF-DONES西班牙）
