Toward a Fully Autonomous, AI-Native Particle Accelerator
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914339143811072 |
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| author | Tennant, Chris |
| author_facet | Tennant, Chris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_17536 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward a Fully Autonomous, AI-Native Particle Accelerator Tennant, Chris Accelerator Physics Artificial Intelligence 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. |
| title | Toward a Fully Autonomous, AI-Native Particle Accelerator |
| topic | Accelerator Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2602.17536 |