| Title |
Optimization of Heavy-Ion Synchrotrons Using Nature-Inspired Algorithms and Machine Learning |
| Authors |
- S. Appel, W. Geithner, S. Reimann, M. Sapinski, R. Singh, D.M. Vilsmeier
GSI, Darmstadt, Germany
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| Abstract |
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.
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| Paper |
download SAPAF02.PDF [2.627 MB / 7 pages] |
| Slides |
download SAPAF02_TALK.PDF [2.642 MB] |
| Export |
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※ EndNote |
| Conference |
ICAP2018 |
| Series |
International Computational Accelerator Physics Conference (13th) |
| Location |
Key West, FL, USA |
| Date |
20-24 October 2018 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Volker RW Schaa (GSI, Darmstadt, Germany); Kyoko Makino (MSU, East Lansing, MI, USA); Pavel Snopok (IIT, Chicago, IL, USA); Martin Berz (MSU, East Lansing, MI, USA) |
| Online ISBN |
978-3-95450-200-4 |
| Received |
16 October 2018 |
| Accepted |
27 January 2019 |
| Issue Date |
04 May 2019 |
| DOI |
doi:10.18429/JACoW-ICAP2018-SAPAF02 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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