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RIS citation export for WEPV010: R&D of the KEK Linac Accelerator Tuning Using Machine Learning

AU  - Hisano, A.
AU  - Iwasaki, M.
AU  - Nagahara, H.
AU  - Nakano, T.
AU  - Nakashima, Y.
AU  - Satake, I.
AU  - Satoh, M.
AU  - Takemura, N.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - R&D of the KEK Linac Accelerator Tuning Using Machine Learning
J2  - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021
CY  - Shanghai, China
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 18
LA  - english
AB  - We have developed a machine-learning-based operation tuning scheme for the KEK e⁻/e⁺ injector linac (Linac), to improve the injection efficiency. The tuning scheme is based on the various accelerator operation data (control parameters, monitoring data and environmental data) of Linac. For the studies, we use the accumulated Linac operation data from 2018 to 2021. To solve the problems on the accelerator tuning of, 1. A lot of parameters (~1000) should be tuned, and these parameters are intricately correlated with each other; and 2. Continuous environmental change, due to temperature change, ground motion, tidal force, etc., affects to the operation tuning; We have developed, 1. Visualization of the accelerator parameters (~1000) trend/correlation distribution based on the dimensionality reduction using Variational Autoencoder (VAE), to see the long-term correlation between the accelerator operation parameters and the environmental data, and 2. Accelerator tuning method using the deep neural network, which is continuously updated with the short-term accelerator data to adapt the environment changes. In this presentation, we report the current status of the R&D.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 640
EP  - 644
KW  - injection
KW  - linac
KW  - network
KW  - operation
KW  - electron
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-WEPV010
UR  - https://jacow.org/icalepcs2021/papers/wepv010.pdf
ER  -