Toward a Fully Autonomous, AI-Native Particle Accelerator

TL;DR

This paper envisions the development of fully autonomous, AI-native particle accelerators through AI co-design, integrating advanced control, diagnostics, and adaptive learning to enhance performance and reliability.

Contribution

It proposes a comprehensive roadmap for designing future accelerators as AI-native platforms from inception, emphasizing nine key research areas for autonomous operation.

Findings

Identification of nine critical research thrusts for AI-native accelerators

Conceptual framework for AI co-design in accelerator development

Roadmap to achieve autonomous, high-performance particle accelerators

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.