AI-Ready Control System for the Fermilab Accelerator Complex

TL;DR

This paper discusses the development of an AI-ready control system for Fermilab's accelerator complex, focusing on infrastructure, data quality, and workflow integration to enable reliable AI/ML applications in accelerator operations.

Contribution

It introduces a comprehensive framework combining MLOps, data quality standards, and LLM integration to facilitate AI/ML deployment in accelerator control systems.

Findings

Established a MLOps framework for accelerator AI/ML lifecycle

Defined data quality standards for trustworthy AI applications

Demonstrated AI/ML use cases in beam diagnostics and control

Abstract

Reliable, high-intensity operation of the Fermilab Accelerator Complex is critical to the success of the Long-Baseline Neutrino Facility and Deep Underground Neutrino Experiment. We describe the requirements and infrastructure necessary to support routine use of artificial intelligence and machine learning (AI/ML) in the accelerator control system. Three capabilities are identified: a machine learning operations (MLOps) framework standardizing the lifecycle of AI/ML automation from data management through deployment and monitoring; a data quality framework defining and enforcing standards required to build trustworthy AI/ML applications; and workflow integration with large language models to assist physicists, engineers, and operators with information retrieval, code development, and routine analysis. Use cases spanning beam diagnostics, beam control, and support system automation illustrate the technical requirements across the complex.