JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{hisano:icalepcs2021-wepv010,
author = {A. Hisano and M. Iwasaki and H. Nagahara and T. Nakano and Y. Nakashima and I. Satake and M. Satoh and N. Takemura},
% author = {A. Hisano and M. Iwasaki and H. Nagahara and T. Nakano and Y. Nakashima and I. Satake and others},
% author = {A. Hisano and others},
title = {{R&D of the KEK Linac Accelerator Tuning Using Machine Learning}},
booktitle = {Proc. ICALEPCS'21},
pages = {640--644},
eid = {WEPV010},
language = {english},
keywords = {injection, linac, network, operation, electron},
venue = {Shanghai, China},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {18},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {03},
year = {2022},
issn = {2226-0358},
isbn = {978-3-95450-221-9},
doi = {10.18429/JACoW-ICALEPCS2021-WEPV010},
url = {https://jacow.org/icalepcs2021/papers/wepv010.pdf},
abstract = {{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.}},
}