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...
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...
