Author: Satake, I.
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WEPV010 R&D of the KEK Linac Accelerator Tuning Using Machine Learning 640
  • A. Hisano, M. Iwasaki
    OCU, Osaka, Japan
  • H. Nagahara, Y. Nakashima, N. Takemura
    Osaka University, Institute for Datability Science, Oasaka, Japan
  • T. Nakano
    RCNP, Osaka, Japan
  • I. Satake, M. Satoh
    KEK, Ibaraki, Japan
  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.  
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About • Received ※ 10 October 2021       Revised ※ 19 October 2021       Accepted ※ 21 November 2021       Issue date ※ 11 January 2022
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