ICALEPCS2025 - Proceedings
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URLhttps://doi.org/10.18429/JACoW-ICALEPCS2025-FRAG001
TitleML-assisted beamline optimization at LCLS
Authors
  • Z. Lentz, R. Tang-Kong
    Linac Coherent Light Source
  • M. Rakitin, T. Morris
    National Synchrotron Light Source II
  • S. Miskovich
    SLAC National Accelerator Laboratory
AbstractLCLS 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.
Paperdownload: FRAG001.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference20th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'25)
Series
LocationChicago, IL, USA
Date20-26 Sep 2025
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardKent Wootton (ANL), Jeffery Biss (FNAL), Gustavo Bruno (ANL), Jeff Kirchman (ANL), Kelly Jaje (ANL), Denise Skiadopoulos (ANL), SuYin Grass Wang (ANL)
Online ISBN978-3-95450-255-4
Online ISSN2226-0358
Received15 September 2025
Revised21 September 2025
Accepted27 October 2025
Issued25 November 2025
DOI10.18429/JACoW-ICALEPCS2025-FRAG001
Pages1873-1877
Copyright Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.