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URLhttps://doi.org/10.18429/JACoW-IPAC2025-THPS110
TitleAdvancing accelerator science through data-intensive research and training
Authors
  • C. Welsch
    University of Liverpool
AbstractThe Liverpool Centre for Doctoral Training in Innovation in Data Intensive Science (LIV.INNO) has made significant progress in applying data-intensive methods to accelerator research. This contribution presents research outcomes from the center with a focus on two key projects. The first focuses on optimizing 3D imaging for medical and industrial applications, integrating Monte Carlo simulations and advanced collimation techniques to enhance low-dose, portable X-ray systems, with implications for wider accelerator diagnostics. The second lever-ages deep learning models to reconstruct transverse beam distributions at CERN, addressing challenges in image distortion from multimode optical fibers under high-radiation conditions. The results are connected with wider progress made in machine learning and artificial intelligence for particle accelerators. Furthermore, the paper summarizes the outcomes of several key LIV.INNO events: the STFC Summer School on Data Intensive Science, the LIV.INNO 2024 Industry Showcase and the 2025 AI for Innovation Summit.
Paperdownload: THPS110.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference16th International Particle Accelerator Conference
Series
LocationTaipei, Taiwan
Date01-06 Jun 2025
PublisherJACoW Publishing, Geneva, Switzerland
Editorial Board* Ming-Chyuan Lin - National Synchrotron Radiation Research Center * Yoichi Sato - KEK * Jui-Che Huang - National Synchrotron Radiation Research Center * David Button - Australian Nuclear Science and Technology Organisation * Ping-Shun Chuang - National Synchrotron Radiation Research Center
Online ISBN978-3-95450-248-6
Online ISSN2673-5490
Received28 May 2025
Revised31 May 2025
Accepted05 June 2025
Issued05 November 2025
DOI10.18429/JACoW-IPAC2025-THPS110
Pages3193-3196