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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Machine learning (ML)-based black-box optimization algorithms have demonstrated significant improvements in accelerator optimization speed, often by orders of magnitude. However, deploying these algorithms in real-time facility control remains challenging due to the specialized expertise and infrastructure required. To bridge this gap, we introduce the Xopt ecosystem, a versatile suite of tools designed to make advanced ML-based optimization accessible to the broader accelerator community. This ecosystem includes Xopt, a modular Python framework that facilitates the integration of ML-based optimization algorithms with arbitrary control problems, and Badger, a graphical user interface built on top of Xopt, which enables seamless deployment of ML algorithms in real-time control systems. The Xopt ecosystem has been successfully applied towards solving challenging real-time control problems at leading international accelerator facilities, including SLAC, LBNL, Argonne, Fermilab, BNL, DESY, and ESRF, demonstrating its effectiveness in real-world optimization tasks. In this presentation, we provide an overview of Xopt’s capabilities and illustrate its impact through case studies from SLAC accelerator facilities including LCLS, LCLS-II, and FACET-II.

Roussel, Ryan [SLAC]↗
DOE OSTI2023

Research in Advanced Artificial Intelligence Techniques for Modern Accelerator Control

The major goals for the project were to remain in contact with accelerator stewardship customers, develop and test new techniques based to further improve accelerator and peripheral systems, and train early career persons to participate on the project and teach them aspects of the field of accelerator science and technology.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Accelerating Control Systems with GitOps: A Path to Automation and Reliability

GitOps is a foundational approach for modernizing infrastructure by leveraging Git as the single source of truth for declarative configurations. The poster explores how GitOps transforms traditional control system infrastructure, services and applications by enabling fully automated, auditable, and version-controlled infrastructure management. Cloud-native and containerized environments are shifting the ecosystem not only in the IT industry but also within the computational science field, as is the case of CERN and Diamond Light Source among other Accelerator/Science facilities which are slowly shifting towards modern software and infrastructure paradigms. The ACORN project, which aims to modernize Fermilab’s control system infrastructure and software is implementing proven best-practices and cutting-edge technology standards including GitOps, containerization, infrastructure as code and modern data pipelines for control system data acquisition and the inclusion of AI/ML in our accelerator complex.

Gonzalez, M. [Fermilab]↗
DOE OSTI2025

A Data Science and Machine Learning Platform Supporting Large Particle Accelerator Control and Diagnostics Applications Final Report: SBIR Initial Phase II DE-SC0022583

The Machine Learning Data Platform (MLDP) is a product providing full-stack support for data science, Machine Learning, and Artificial Intelligence (ML/AI) applications at particle accelerator and large experimental physics facilities. It supports ML/AI applications from front-end, high-speed acquisition of heterogeneous, time-series data, through data archiving and management, to back-end analysis. The MLDP embodies a “data-science ready” platform for data analysis and ML/AI applications in diagnosis, modelling, control, and optimization of these facilities. It provides data scientists and applications a consistent, datacentric interface to archive data standardizing implementation and deployment of ML/AI algorithms to different operations configurations within the same facility, or between facilities. Being an open-source, public-domain project, the MLDP is intended for broadest possible impact by increasing accessibility and minimizing the required expertise for installation and operation. The MLDP can also be deployed at user facilities for experimental data collection, archiving, and analysis. It is capable of acquisition and archiving of heterogeneous data from experimental equipment (e.g., images, arrays, structures, etc.) along with system hardware configurations (e.g., scalars, tables), control system process variables, and any metadata required for provenance. Thus, the MLDP can manage experimental data through its entire lifecycle, from acquisition and archiving, through analysis and investigation, to release and final publication.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

BOOSTR: A Dataset for Accelerator Control Systems

The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab's Rapid-Cycling Synchrotron (RCS) operating at 15~Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster's gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

BOOSTR: A Dataset for Accelerator Control Systems

The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab’s Rapid-Cycling Synchrotron (RCS) operating at 15 Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster’s gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.

Kafkes, Diana (ORCID:000000021716463X)↗
DOE OSTI2025

Fermilab's controls development with virtual accelerator

Control Systems development is often the last thing considered when designing and building new equipment, e.g. a new detector or superconducting RF LINAC; however when the new equipment is installed, it is the first thing desired to be operational for testing. Due to frequent delays in building new equipment and project deadlines, control system development and testing is often curtailed. A way to alleviate this problem is to simulate the control system, though this will be challenging for complex systems.The Fermilab PIP-II (proton improvement plan - II) project is being constructed at Fermilab to deliver $800\,MeV$ protons of $>1\,MW$ beam power to replace the present LINAC for the remainder of the existing accelerator complex. The new LINAC consists of a warm front end (WFE), 23 superconducting RF cryomodules (of 5 types), and a beam transfer line (BTL) to the existing complex.The accelerator physics group has a parallel project to create a digital twin (DT) of the PIP-II accelerator. We have coupled the EPICS controls to this DT and are developing both the DT and EPICS software in parallel. This will allow us to develop the EPICS software framework, the HMIs, sequences, high level physics applications, and other services for use in a fully functional control system.This presentation will detail the work that we have performed to date and show demonstrations of controlling and monitoring the status of the accelerator, as well as future plans for this work.

Hanlet, Pierrick [Fermilab]↗
DOE OSTI2025

Modernizing to an AI-Ready Control System

The Accelerator Controls Operations Research Network (ACORN) project will modernize the accelerator control system and upgrade power supplies to enable future operations of the Fermilab Accelerator Complex with megawatt proton beams. By focusing on MLOps, ACORN enables full integration of AI into the control system to allow safe and reliable improvements to accelerator beam operations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Advanced Control Methods for Particle Accelerators (ACM4PA) 2019

Los Alamos is currently developing novel particle accelerator controls and diagnostics algorithms to enable higher quality beams with lower beam losses than is currently possible. The purpose of this workshop was to consider tuning and optimization challenges of a wide range of particle accelerators including linear proton accelerators such as the Los Alamos Neutron Science Center (LANSCE), rings such as the Advanced Photon Source (APS) synchrotron, free electron lasers (FEL) such as the Linac Coherent Light Source (LCLS) and LCLS-II, the European X-ray Free Electron Laser (EuXFEL), the Swiss FEL, and the planned MaRIE FEL, and plasma wake- eld accelerators such as FACET, FACET-II, and AWAKE at CERN. One major challenge is an the ability to quickly create very high quality, extremely intense, custom current and energy pro le beams while working with limited real time non-invasive diagnostics and utilizing time-varying uncertain initial beam distributions and accelerator components. Currently, a few individual accelerator labs have been developing and applying their own diagnostics tools and custom control and ML algorithms for au- tomated machine tuning and optimization. The goal of this workshop was to bring together a group of accelerator physicists and accelerator related control and ML experts in order to de ne which controls and diagnostics would be most useful for existing and future accelerators and to create a plan for developing a new family of algorithms that can be shared and maintained by the community.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Acceleration of Glass Alteration Rates Induced by Zeolite Seeds at Controlled pH

As the glass-water reaction proceeds, certain glass compositions undergo a delayed acceleration in the alteration rate – a phenomenon that is commonly referred to as Stage III behavior – which generally coincides with zeolite formation. However, studying Stage III in the laboratory is difficult because the time scale required to observe Stage III behavior can vary from months to several years, and the solution conditions that initiate this acceleration remain poorly understood. Consequently, the development of a corrosion test method capable of quantifying Stage III rates in a timely manner would be of great benefit to both the scientific community and policy makers concerned with nuclear waste disposal. In this work, we induced and measured the acceleration in the corrosion rate of the AFCI glass by seeding static corrosion tests with zeolite Na-P2 at fixed pH90°C values ranging from 9.5 to 11.5. The induced Stage III rates increased linearly with increasing pH and were measured to be between the forward and residual rates of AFCI. Tests designed to compare induced Stage III rates obtained for different seeding times showed that the induced Stage III rates were faster in the absence of an alteration layer. In summary, the use of the seeded test method allows for a conservative quantification of Stage III rates in a relatively short, one-month time period.

Neeway, James J.↗
DOE OSTI2026

AI-Ready Control System for the Fermilab Accelerator Complex

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.

43 PARTICLE ACCELERATORS↗