Machine-learning approach for operating electron beam at KEK e^-/e^+ injector Linac
arxiv(2024)
摘要
In current accelerators, numerous parameters and monitored values are to be
adjusted and evaluated, respectively. In addition, fine adjustments are
required to achieve the target performance. Therefore, the conventional
accelerator-operation method, in which experts manually adjust the parameters,
is reaching its limits. We are currently investigating the use of machine
learning for accelerator tuning as an alternative to expert-based tuning. In
recent years, machine-learning algorithms have progressed significantly in
terms of speed, sensitivity, and application range. In addition, various
libraries are available from different vendors and are relatively easy to use.
Herein, we report the results of electron-beam tuning experiments using
Bayesian optimization, a tree-structured Parzen estimator, and a covariance
matrix-adaptation evolution strategy. Beam-tuning experiments are performed at
the KEK e^-/e^+ injector Linac to maximize the electron-beam charge and
reduce the energy-dispersion function. In each case, the performance achieved
is comparable to that of a skilled expert.
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