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[2602.17536] Toward a Fully Autonomous, AI-Native Particle Accelerator
Summary
This paper outlines a vision for fully autonomous, AI-native particle accelerators, emphasizing AI co-design for optimal performance and minimal human intervention.
Why It Matters
The development of AI-native particle accelerators could revolutionize scientific research by enhancing operational efficiency and output. This approach addresses the need for advanced facilities that leverage AI from the ground up, potentially leading to breakthroughs in various fields of physics and engineering.
Key Takeaways
- AI co-design can optimize particle accelerator performance.
- Future accelerators should be designed as AI-native platforms.
- Nine critical research areas are identified for advancing autonomous operation.
Physics > Accelerator Physics arXiv:2602.17536 (physics) [Submitted on 19 Feb 2026] Title:Toward a Fully Autonomous, AI-Native Particle Accelerator Authors:Chris Tennant View a PDF of the paper titled Toward a Fully Autonomous, AI-Native Particle Accelerator, by Chris Tennant View PDF HTML (experimental) Abstract: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. Comments: Subjects: Accelerator Physics (physics.acc-ph); Artificial Intelligence (cs.AI) Report number: JLAB-ACC-26-4590 Cite as: arXiv:2602.17536 [physics.acc-ph] (or arXiv:2602.17536v1 [physics.acc-ph] for this version) https://doi...