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 SAPAF02 [2.642 MB]
|
|
DOI • |
reference for this paper
※ https://doi.org/10.18429/JACoW-ICAP2018-SAPAF02
|
|
About • |
paper received ※ 16 October 2018 paper accepted ※ 27 January 2019 issue date ※ 26 January 2019 |
|
Export • |
reference for this paper using
※ BibTeX,
※ LaTeX,
※ Text/Word,
※ RIS,
※ EndNote (xml)
|
|
|