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@ARTICLE{Awal:1006970,
author = {Awal, A. and Hetzel, J. and Gebel, R. and Kamerdzhiev, V.
and Pretz, Jörg},
title = {{O}ptimization of the injection beam line at the {C}ooler
{S}ynchrotron {COSY} using {B}ayesian {O}ptimization},
journal = {Journal of Instrumentation},
volume = {18},
number = {04},
issn = {1748-0221},
address = {London},
publisher = {Inst. of Physics},
reportid = {FZJ-2023-01921},
pages = {P04010 -},
year = {2023},
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.},
cin = {IKP-2 / IKP-4},
ddc = {610},
cid = {I:(DE-Juel1)IKP-2-20111104 / I:(DE-Juel1)IKP-4-20111104},
pnm = {612 - Cosmic Matter in the Laboratory (POF4-612)},
pid = {G:(DE-HGF)POF4-612},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000986916100003},
doi = {10.1088/1748-0221/18/04/P04010},
url = {https://juser.fz-juelich.de/record/1006970},
}