ICALEPCS2025 - Proceedings
Chicago, IL, USA
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URLhttps://doi.org/10.18429/JACoW-ICALEPCS2025-WEMR005
TitleMachine learning–based longitudinal phase space tuning for X-ray free-electron laser
Authors
  • Z. Zhu, A. Edelen
    SLAC National Accelerator Laboratory
AbstractPrecise control of the longitudinal phase space (LPS) in X-ray free-electron laser (XFEL) is critical for optimizing beam qualities and X-ray pulses properties required by the experimental stations. We present results of using machine learning techniques for LPS shaping and control with Bayesian optimization.
Paperdownload: WEMR005.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
Received10 October 2025
Revised21 October 2025
Accepted31 October 2025
Issued25 November 2025
DOI10.18429/JACoW-ICALEPCS2025-WEMR005
Pages1001-1004
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