Author: Vilsmeier, D.M.
Paper Title Page
SAPAF02 Optimization of Heavy-Ion Synchrotrons Using Nature-Inspired Algorithms and Machine Learning 15
  • S. Appel, W. Geithner, S. Reimann, M. Sapinski, R. Singh, D.M. Vilsmeier
    GSI, Darmstadt, Germany
  The application of machine learning and nature-inspired optimization methods, like for example genetic algorithms (GA) and particle swarm optimization (PSO) can be found in various scientific/technical areas. In recent years, those approaches are finding application in accelerator physics to a greater extent. In this report, nature-inspired optimization as well as the machine learning will be shortly introduced and their application to the accelerator facility at GSI/FAIR will be presented. For the heavy-ion synchrotron SIS18 at GSI, the multi-objective GA/PSO optimization resulted in a significant improvement of multi-turn injection performance and subsequent transmission for intense beams. An automated injection optimization with genetic algorithms at the CRYRING@ESR ion storage ring has been performed. The usage of machine learning for a beam diagnostic application, where reconstruction of space-charge distorted beam profiles from ionization profile monitors is performed, will also be shown. First results and the experience gained will be presented.  
slides icon Slides SAPAF02 [2.642 MB]  
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About • paper received ※ 16 October 2018       paper accepted ※ 27 January 2019       issue date ※ 26 January 2019  
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