Author: Schoefer, V.
Paper Title Page
THAL04 Machine Learning Based Tuning and Diagnostics for the ATR Line at BNL 803
  • J.P. Edelen, K. Bruhwiler, E.G. Carlin, C.C. Hall
    RadiaSoft LLC, Boulder, Colorado, USA
  • K.A. Brown, V. Schoefer
    BNL, Upton, New York, USA
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682.
Over the past several years machine learning has increased in popularity for accelerator applications. We have been exploring the use of machine learning as a diagnostic and tuning tool for transfer line from the AGS to RHIC at Brookhaven National Laboratory. In our work, inverse models are used to either provide feed-forward corrections for beam steering or as a diagnostic to illuminate quadrupole magnets that have excitation errors. In this talk we present results on using machine learning for beam steering optimization for a range of different operating energies. We also demonstrate the use of inverse models for optical error diagnostics. Our results are from studies that use combine simulation and measurement data.
slides icon Slides THAL04 [4.845 MB]  
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About • Received ※ 10 October 2021       Revised ※ 22 October 2021       Accepted ※ 06 February 2022       Issue date ※ 01 March 2022
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