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...
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...
