ICAP2018 - List of Keywords (injection)

Paper	Title	Other Keywords	Page
SAPAF02	Optimization of Heavy-Ion Synchrotrons Using Nature-Inspired Algorithms and Machine Learning	emittance, simulation, synchrotron, space-charge	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)

Paper

Title

Other Keywords

Page

SAPAF02

Optimization of Heavy-Ion Synchrotrons Using Nature-Inspired Algorithms and Machine Learning

emittance, simulation, synchrotron, space-charge

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)