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RIS citation export for TH2C02: Machine Learning-Assisted Beam Operation at SuperKEKB and Linac at KEK

AU  - Mitsuka, G.
AU  - Iida, N.
AU  - Kato, S.K.
AU  - Natsui, T.
AU  - Satoh, M.
ED  - Schaa, Volker R.W.
ED  - Batten, Tonia
ED  - Bree, Michael
ED  - Petit-Jean-Genaz, Christine
ED  - Hunter, Darren
TI  - Machine Learning-Assisted Beam Operation at SuperKEKB and Linac at KEK
J2  - Proc. of IBIC2023, Saskatoon, Canada, 10-14 September 2023
CY  - Saskatoon, Canada
T2  - International Beam Instrumentation Conference
T3  - 12
LA  - english
AB  - Hundreds to thousands of tuning parameters must be optimized for each operating condition to obtain the best performance from an accelerator. In the past, experts made decisions based on their experience on which tuning parameters contributed the most to the performance and adjusted them sequentially. On the other hand, accelerator tuning approaches based on machine learning, which has become much easier to handle, have been studied intensively in recent years. We have been developing a beam-tuning tool based on Bayesian optimization for boosting the SuperKEKB accelerator. In this presentation, we will report on the latest status of the beam test of the positron-beam-yield maximization and dispersion tuning at the KEK injector as the first development step.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
ER  -