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URLhttps://doi.org/10.18429/JACoW-LINAC2024-MOAA012
TitleAutomation of RF tuning for medical accelerators
Authors
  • F. O'Shea, A. Edelen
    SLAC National Accelerator Laboratory
  • J. Edelen, M. Henderson
    RadiaSoft LLC
  • J. Diaz Cruz
    University of New Mexico
AbstractRadiaSoft is developing machine learning methods to improve the operation and control of industrial accelerators. Because industrial systems typically suffer from a lack of instrumentation and a noisier environment, advancements in control methods are critical for optimizing their performance. In particular, our recent work has focused on the development of pulse-to-pulse feedback algorithms for use in dose optimization for FLASH radiotherapy. The PHASER (pluridirectional high-energy agile scanning electronic radiotherapy) system is of particular interest due to the need to synchronize 16 different accelerators all with their own noise characteristics. This presentation will provide an overview of the challenges associated with RF tuning for a PHASER-like system, a description of the model used to evaluate different control schema, and our initial results using conventional methods and machine learning methods.
Paperdownload: MOAA012.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference32nd Linear Accelerator Conference (LINAC2024)
Series
LocationChicago, IL, USA
Date25-30 Aug 2024
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardKelly Jaje (ANL), Kip Bishofberger (LANL), Ryan Brody (ANL), Gustavo Bruno (ANL), Jan Chrin (PSI), Cathy Eyberger (ANL), Maggie Montes (SLAC), Denise Skiadopoulos (ANL), Ling Wang (MSU/FRIB), Kent Wootton (ANL)
Online ISBN978-3-95450-219-6
Online ISSN2226-0366
Received19 August 2024
Revised27 August 2024
Accepted27 August 2024
Issued23 October 2024
DOI10.18429/JACoW-LINAC2024-MOAA012
Pages47-49