xLight Inc. is developing an accelerator-driven extreme ultraviolet (EUV) light source for the semiconductor manufacturing industry. This machine must operate autonomously, a goal we believe will be achieved through AI/ML techniques. In this talk, we offer an industry perspective on the proposal for a dedicated AI/ML accelerator test facility. We share some of the unique challenges in...
Developing a multifaceted ML tool for day-to-day operation of a particle accelerator requires close collaboration with the machine operators to incorporate their experience and feedback. The tool must also ensure that all safety protocols and operational redundancies established by the operations team are fully integrated. In the initial part of this talk we briefly present the framework of an...
This talk will highlight the operational challenges within the SNS complex. We will cover facility priorities, as illustrated by on-going efforts to implement ML solutions for beam control and facility health. We also describe capabilities that we expect to have benefits and transferability to SNS operations.
AI and machine learning tools have the potential to significantly improve operational efficiency, but there are important challenges standing in the way of their deployment and widespread adoption. Rather than focusing on new AI algorithms or applications, this discussion will examine what SLAC’s operators have learned through early experience with these technologies: where AI can provide...
At LCLS, delivering a stable X-ray beam to the experiment still depends on skilled operator time across a long chain of accelerator and beamline systems; and with LCLS-II-HE, manual setup is becoming harder to sustain. At MFX we are building autoMFX around three pieces that are starting to work together: BES-funded ILLUMINE for autonomous photon-beam tuning; language-model assistants that let...
We will discuss the data pipeline that supports user experiments at neutron scattering facilities, and how AI workflows can help accelerate science. AI can play a role in accelerating most areas of our data pipeline. While numerous efforts are ongoing to address individual needs, we will focus on the common infrastructure that an AI-enabled user facility requires and the current open questions...
Next-generation long-baseline neutrino experiments aim to achieve multi-MW proton beam power while reducing accelerator-induced systematic uncertainties. At Fermilab, the LBNF beamline is designed for 1.2 MW operation with PIP-II and is upgradeable to 2.4 MW. DUNE will probe the three-flavor neutrino paradigm and search for CP violation, requiring precise neutrino-flux normalization and...
As AI/ML‑enabled workflows become increasingly central to accelerator and photon‑science experiments, the need for flexible, metadata‑rich, and automation‑friendly control systems has grown substantially. Bluesky provides a modern Python‑based framework for experiment orchestration, data acquisition, and real‑time feedback, making it a strong candidate for integration with next‑generation...
As fusion devices move toward reactor-scale conditions, robust, real-time plasma control is essential for sustaining high-performance operation and tight stability margins. This talk presents recent AI-driven control advances on the DIII-D and KSTAR tokamaks, highlighting successful demonstrations of RMP-induced ELM control [1], preemptive tearing mode suppression [2], and...
High-fidelity virtual replicas of accelerator facilities, otherwise known as digital twins, are becoming increasingly desirable tools that many labs are seeking to add to their arsenal. Numerous hurdles can prevent the successful integration of a digital twin into the day-to-day operation of the machine, including control system integration, model fidelity, and processing speed, not to mention...
Artificial intelligence and machine learning are becoming increasingly practical tools for accelerator operation and diagnostics. This talk will present an overview of ongoing AI/ML activities at the Advanced Photon Source. Topics include differentiable simulation methods for beam sigma matrix reconstruction, hysteresis-aware modeling and control, anomaly detection in power-supply and...
The BNL Hadron Injector Complex serves as an active testbed for applying AI/ML techniques to accelerator operations, including beam tuning and optimization, predictive modeling, anomaly detection, virtual diagnostics, and operator decision support. Here we provide an overview of ongoing AI/ML efforts at BNL and the practical experience gained from integrating machine learning with accelerator...
We present the first live deployment of reinforcement learning for closed-loop control of an operational cyclotron, demonstrated at the PSI Injector-2 during a 12-day beam-development campaign. The agent controlled multiple magnetic and RF actuators using phase, loss, and current diagnostics to minimize phase deviations and radial losses directly on the machine.
For a fixed operating...
The Argonne Wakefield Accelerator (AWA) is a flexible user facility and testbed for accelerator physics, radiofrequency technology, and advanced accelerator concepts. Recent efforts include migrating controls to EPICS for greater standardization and portability across facilities, alongside the addition of a new low-level RF system. These hardware upgrades have enabled the deployment of...
Operating scientific user facilities demands continuous optimization and rapid decision-making to maximize throughput and scientific output. To address this, we present major updates to the LUME ecosystem that standardize the implementation and deployment of virtual accelerators and digital twins across heterogeneous simulation backends and control system interfaces at SLAC National...
Machine learning (ML) is emerging as a powerful tool for improving accelerator operations by reducing tuning time, increasing machine reliability, and enabling more autonomous operation. At the National Synchrotron Light Source II (NSLS-II), we have applied ML to two important operational tasks: accelerator tuning and anomaly detection. In this talk, I will present our work on using ML for...
Agentic AI has the potential to assist accelerator operations through natural-language interaction, automated analysis, and execution of complex workflows. Yet deploying such capabilities at operating user facilities remains challenging. Development opportunities on production machines are limited, operational risk is high, and software developed for one facility often requires substantial...
Recent advances in artificial intelligence and machine learning have created new opportunities for autonomous operation of accelerator facilities. While significant effort has focused on developing optimization algorithms and AI-enabled control frameworks, comparatively less attention has been devoted to the supervisory software architecture required to coordinate autonomous multi-section...
Through years of HEP core investment and the AI-Hardware project, we have established a machine-learning + low-level RF (ML+LLRF) foundation for real-time control of lasers and accelerator subsystems. The foundation is built on the open, reconfigurable LBNL Marble platform and a set of general-purpose DSP repositories designed to be portable across plant classes, rather than tied to a single...
FACET-II requires operators to conduct multiple tasks for both injector startup and optimal performance at the experimental area. Injector startup tasks consume hours of beamtime, a significant cost for a multi-purpose facility. Moreover, beam tuning for plasma wakefield acceleration at the experimental area is a challenging and time-consuming task due to the longitudinal-transverse coupling...
Reinforcement learning (RL) is a unique learning
paradigm inspired by the behaviour of animals and humans to learn to solve tasks autonomously. Learning occurs
through interactions with an environment, exploring, and
evaluating strategies under various conditions. RL excels in
complex environments, can handle delayed consequences,
and is able to learn solely from experience without...
Safety-critical systems — particle accelerators, reactors, enrichment cascades — occupy a category apart. Failure is not a degraded user experience but a physical hazard, and the properties that define these systems (hard real-time constraints, formally verified interlocks, regulatory traceability, and a low tolerance for opacity) are precisely the properties that make standard...
IGNITE is a multi-modal world model for full discharge simulation, trained on a large-scale dataset of DIII-D experimental data. This world model has been developed as part of the DOE Fusion Science & Technology Roadmap [1]. Inspired by the success of scientific world models for weather and climate forecasting [2,3,4], IGNITE is an end-to-end AI model that ingests heterogeneous diagnostic...
The design of modern accelerator and RF components demands high-fidelity, multi-physics modeling at scale, motivating the tight integration of AI/ML methods with high-performance computing (HPC). ACE3P, a comprehensive suite of conformal, high-order, parallel finite-element codes developed at SLAC, provides coupled electromagnetic, thermal, and mechanical analysis for a broad range of...
Good progress has been made in the online deployment of the AI-ML tools developed over the past few years at the ATLAS facility at Argonne. A dedicated AI-ML interface has been developed and integrated into the control system. It allows the user to simply select the beamline to tune, the task to perform, then hit the “execute” button and watch the results. In addition, a virtual accelerator...
