Speaker
Description
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 efficient online tuning of the injector and storage ring. I will also present a model-based anomaly detection method that monitors magnet performance and identifies overheating issues, providing early alerts so engineers can flush the abnormal magnets before problems become more serious. The presentation will also describe how these ML tools are integrated into the NSLS-II control system and share practical lessons from online deployment, including real-time data access, safe operation, and interaction with operators. These experiences help identify the infrastructure and software capabilities needed to support future AI/ML-enabled accelerator facilities.
