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RIS citation export for SAPAF02: Optimization of Heavy-Ion Synchrotrons Using Nature-Inspired Algorithms and Machine Learning

AU  - Appel, S.
AU  - Geithner, W.
AU  - Reimann, S.
AU  - Sapinski, M.
AU  - Singh, R.
AU  - Vilsmeier, D.M.
ED  - Schaa, Volker RW
ED  - Makino, Kyoko
ED  - Snopok, Pavel
ED  - Berz, Martin
TI  - Optimization of Heavy-Ion Synchrotrons Using Nature-Inspired Algorithms and Machine Learning
J2  - Proc. of ICAP2018, Key West, FL, USA, 20-24 October 2018
CY  - Key West, FL, USA
T2  - International Computational Accelerator Physics Conference
T3  - 13
LA  - english
AB  - 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.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 15
EP  - 21
KW  - injection
KW  - emittance
KW  - simulation
KW  - synchrotron
KW  - space-charge
DA  - 2019/01
PY  - 2019
SN  - 978-3-95450-200-4
DO  - DOI: 10.18429/JACoW-ICAP2018-SAPAF02
UR  - http://jacow.org/icap2018/papers/sapaf02.pdf
ER  -