Speaker
Description
LCLS is currently developing and deploying beamline optimization techniques at our x-ray end-stations. This is an increasingly important topic at LCLS as it fully leverages its new high rep-rate superconducting beam. The increased throughput of LCLS-II shifts the performance bottleneck to on-shift setup time. As part of the Illumine collaboration, LCLS is leveraging Bayesian optimization techniques with on-the-fly machine learning in conjunction with more conventional iterative alignment and digital twin techniques to automatically optimize the beam quality and streamline common elements of experiment setup. This paper will go over how it works, what worked well, challenges faced, and more from a controls perspective.
Funding Agency
Work supported by US DOE Office of Science BES Award Number FWP-101101
Use of LCLS supported by U.S. D.O.E Contract DE-AC02-76SF00515
Use of NSLS-II supported by U.S. D.O.E Contract DE-SC0012704
