Toward a Fully Autonomous, AI-Native Particle Accelerator

• URL: http://arxiv.org/abs/2602.17536v1
• Date: Thu, 19 Feb 2026 16:49:36 GMT
• Title: Toward a Fully Autonomous, AI-Native Particle Accelerator
• Authors: Chris Tennant,
• Abstract summary: We propose that future facilities be designed through artificial intelligence (AI) co-design.<n>Rather than retrofitting AI onto human-centric systems, we envision facilities designed from the ground up as AI-native platforms.<n>This roadmap aims to guide the accelerator community toward a future where AI-driven design and operation deliver unprecedented science output and reliability.
• Score: 0.342658286826597
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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