Optimization of the injection beam line at the Cooler Synchrotron COSY using Bayesian Optimization

被引：3

作者：

Awal, A. ^{[1
,2
]}

Hetzel, J. ^{[2
]}

Gebel, R. ^{[2
]}

Kamerdzhiev, V. ^{[2
]}

Pretz, J. ^{[1
,3
]}

机构：

[1] Rhein Westfal TH Aachen, Phys Inst B 3, Otto Blumenthal Str, D-52056 Aachen, Germany

[2] GSI Helmholtzzentrum Schwerionenforsch, Planckstr 1, D-64291 Darmstadt, Germany

[3] Forschungszentrum Julich GmbH, Inst Kernphys, Wilhelm Johnen Str, D-52428 Julich, Germany

来源：

JOURNAL OF INSTRUMENTATION | 2023年 / 18卷 / 04期

关键词：

Accelerator Applications; Accelerator modelling and simulations (multi-particle dynamics; single-particle dynamics); Analysis and statistical methods;

D O I：

10.1088/1748-0221/18/04/P04010

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

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 Julich, 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.

引用

页数：9

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