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URLhttps://doi.org/10.18429/JACoW-ICALEPCS2025-WEMR002
TitleAI and ML integration for beamline optimization and virtual assistance at the SOLARIS synchrotron
Authors
  • M. Szczepanik, M. Piekarski, M. FaƂowski
    SOLARIS National Synchrotron Radiation Centre
AbstractThe future of synchrotron beamline operations is poised for a transformative leap with advancements in artificial intelligence (AI) and machine learning (ML). While SOLARIS National Synchrotron Radiation Centre* has yet to integrate these technologies, their potential to revolutionize experiments, data analysis, and user interactions is immense. AI-driven automation promises real-time assistance in optimizing beamline experiments, minimizing manual intervention while enhancing precision. Machine learning algorithms will unlock deeper insights from complex datasets, facilitating faster, more accurate interpretations. Additionally, intelligent virtual agents could redefine how researchers interact with beamline controls, offering predictive guidance and adaptive optimization. As SOLARIS expands its capabilities, embracing AI and ML will position it at the forefront of scientific innovation, ensuring seamless, efficient, and accessible synchrotron research for future generations.
Paperdownload: WEMR002.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
Received06 September 2025
Revised22 September 2025
Accepted27 October 2025
Issued25 November 2025
DOI10.18429/JACoW-ICALEPCS2025-WEMR002
Pages993-995