Problem

Particle accelerators are becoming increasingly complex, with millions of sensor channels and thousands of interconnected components requiring precise coordination. Human operators are reaching their limits in managing this complexity, leading to a fundamental question: how can we operate accelerators more autonomously, leveraging AI to manage complexity at machine speed while humans provide strategic oversight? The current approach of retrofitting AI onto human-centric systems is insufficient; a paradigm shift is needed towards designing facilities as AI-native platforms from the ground up, where autonomy is a core design objective.

Method

The paper proposes a vision for "self-driving, natively-AI" particle accelerators, designed through AI co-design from inception. This involves AI jointly optimizing the accelerator lattice, diagnostics, and science application as a unified system. Nine critical research thrusts are outlined: developing agentic control architectures with coordinated AI agents, integrating deep contextual knowledge bases, implementing adaptive learning through reinforcement learning and continuous online refinement, utilizing detailed digital twins for training and validation, establishing comprehensive health monitoring and anomaly detection, designing robust safety frameworks with transparency and fallback strategies, rethinking hardware design for modularity and fault tolerance, fusing diverse multimodal data types into coherent representations, and fostering cross-domain collaboration with fields like robotics.

Results

The expected outcomes of this AI-native approach include a significant increase in science output, enhanced reliability and uptime, and reduced operational costs. AI-driven design can explore novel configurations beyond human intuition, optimizing for performance, reliability, and cost simultaneously. Autonomous operation will enable continuous tuning, real-time issue diagnosis, and safe adaptation to changing conditions, leading to unprecedented science output per unit time, per dollar, or per square foot. The system will learn from experience, continuously improving control policies and identifying potential failures proactively, minimizing downtime and maximizing efficiency.

Implications

Realizing the vision of an autonomous, AI-native particle accelerator will require sustained, coordinated research and significant investment. Challenges include the computational demands for training and deploying AI, the availability of comprehensive data for rare failure modes, and the need for cultural change and workforce development within the accelerator community. New regulatory and certification frameworks for AI in safety-critical roles will also be essential. However, the potential returns are substantial, promising higher facility uptime and performance, reduced operational costs, and the ability to operate future machines of unparalleled complexity. This roadmap aims to guide the accelerator community toward a future where AI-driven design and operation deliver unprecedented scientific discoveries and reliability.