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Toward a Fully Autonomous, AI-Native Particle Accelerator

Chris Tennant

TL;DR

A vision for self-driving particle accelerators that operate autonomously with minimal human intervention is presented and 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 are outlined.

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.

Toward a Fully Autonomous, AI-Native Particle Accelerator

TL;DR

A vision for self-driving particle accelerators that operate autonomously with minimal human intervention is presented and 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 are outlined.

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.
Paper Structure (18 sections, 1 figure)

This paper contains 18 sections, 1 figure.

Table of Contents

  1. Introduction
  2. A New Paradigm for Accelerators: AI-Driven Design and Operations
  3. AI Co-Design: Optimizing the Accelerator and Science Application Together
  4. Staged AI Integration: From Assistance to Autonomy
  5. Natural Language Interfaces and Human-AI Collaboration
  6. Current Progress and Emerging Capabilities
  7. Major AI Research Thrusts Required to Realize the Vision
  8. Agentic AI Control Architecture
  9. Integrated Knowledge Bases and Reasoning
  10. Learning and Adaptive Control
  11. Simulation and Digital Twins for Safe Autonomy
  12. Automated Health Monitoring and Anomaly Response
  13. Safety, Transparency, and Trustworthiness
  14. Modular and Fault-Tolerant Hardware Design
  15. Multimodal Data Fusion
  16. ...and 3 more sections

Figures (1)

  • Figure 1: Illustration of the stages of AI progress in accelerator operations (top) and the nine research thrusts needed to achieve autonomous operation (bottom).