Conveners
FRAG MC13 Artificial Intelligence and Machine Learning
- Mirjam Lindberg (MAX IV Laboratory)
- Mike Fedorov (Lawrence Livermore National Laboratory)
LCLS is currently developing and deploying beamline optimization techniques at their x-ray endstations. This is an increasingly important topic at LCLS as it fully leverages its new high rep-rate superconducting beam. The increased throughput of the LCLS-II era suddenly shifts the performance bottleneck to on-shift beam setup time. As part of the Illumine collaboration, LCLS is leveraging...
Transmutex SA is developing an accelerator-driven system (ADS) designed to generate clean energy while reducing the lifetime of radioactive waste. Such a subcritical reactor concept requires high reliability and a high degree of accelerator automation to ensure operational effectiveness.
To address these demands, a machine learning (ML) methodology was developed and experimentally...
The National Synchrotron Light Source II (NSLS-II) uses a highly stable electron beam to produce high-quality X-ray beams with high brightness and low-emittance synchrotron radiation. The traditional algorithm to stabilize the beam applies singular value decomposition (SVD) on the orbit response matrix to remove noise and extract actions. Supervised learning has been studied on NSLS-II storage...
The deployment of agentic AI systems at the Advanced Light Source (ALS) marks a major step toward autonomous, intelligent facility operations. By connecting large language models (LLMs) with diverse data sources, we are developing agents that not only interface with the control system but also provide a natural language interface for operators and scientists. This allows users to interact with...