Journal Article

FZJ-2023-01921

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Optimization of the injection beam line at the Cooler Synchrotron COSY using Bayesian Optimization

Awal, A.FZJ* ; Hetzel, J.FZJ* ; Gebel, R.FZJ* ; Kamerdzhiev, V.FZJ* ; Pretz, J. (Corresponding author)FZJ*

2023
Inst. of Physics London

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Please use a persistent id in citations: http://hdl.handle.net/2128/34347  doi:10.1088/1748-0221/18/04/P04010

Abstract: The complex non-linear processes in multi-dimensional parameter spaces, that are typical for an accelerator, are a natural application for machine learning algorithms. This paper reports on the use of Bayesian optimization for the optimization of the Injection Beam Line (IBL) of the Cooler Synchrotron storage ring COSY at the Forschungszentrum Jülich, Germany. Bayesian optimization is a machine learning method that optimizes a continuous objective function using limited observations. The IBL is composed of 15 quadrupoles and 28 steerers. The goal is to increase the beam intensity inside the storage ring. The results showed the effectiveness of the Bayesian optimization in achieving better/faster results compared to manual optimization.

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Contributing Institute(s):

1. Experimentelle Hadrondynamik (IKP-2)
2. Kernphysikalische Großgeräte (IKP-4)

Research Program(s):

1. 612 - Cosmic Matter in the Laboratory (POF4-612) (POF4-612)

Appears in the scientific report 2023

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; National-Konsortium ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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