AI-Powered XFEL Laser Operations: Boosting Uptime with Language Models

The operation of large-scale X-ray synchrotrons and free-electron lasers (XFELs) relies on various complex optical laser systems. Beamtime at those facilities is precious; laser failures can cause costly downtime. Even though a laser operator is on-call at all times, it is difficult for a single person to know all the details of every laser system. This project tackles this challenge by developing a novel artificial intelligence-powered assistant to aid on-call laser operators with troubleshooting and maintaining facility laser systems. This project will adjust and further develop advanced large language models (LLMs) to process extensive technical documentation, logbook entries, and control system data to provide expert-level assistance to laser operators. Our project will not only enhance the efficiency and accuracy of troubleshooting but also help to improve technical documentation and generate performance statistics for data-driven decision-making. By harnessing the power of artificial intelligence (AI) and natural language processing (NLP), this project aims to significantly increase the uptime of laser systems and scientific output at Deutsches Elektronen-Syncrotron DESY, paving the way for a future where AI aids in operating the cutting-edge research infrastructure that fuels scientific discovery.