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  • Open Access

Implementation and deployment of an injection tuning tool using Bayesian optimization at the SuperKEKB accelerator

Shinnosuke Kato*

Gaku Mitsuka

  • KEK, Oho, Tsukuba, Ibaraki 305-0801, Japan, and SOKENDAI, Shonan Village, Hayama, Kanagawa 240-0193, Japan

  • *Contact author: s-kato@hep.phys.s.u-tokyo.ac.jp
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Phys. Rev. Accel. Beams 29, 105103 – Published 7 October, 2026

DOI: https://doi.org/10.1103/g523-xdgf

Abstract

As of July 2025, the SuperKEKB accelerator, which collides 7 GeV electrons with 4 GeV positrons to abundantly produce particles such as B mesons and τ leptons, holds the world record for the highest instantaneous luminosity. Continuous operation and upgrades are underway to achieve even higher luminosities. Maintaining a high instantaneous luminosity requires sustaining high beam currents in the storage rings, which in turn demands efficient beam injection from the injector. In particular, a high injection efficiency, defined as the ratio of the beam current successfully accumulated in the ring to the current delivered from the beam transport line, must be ensured. In the present study, we developed a tool to automate the injection tuning process using Bayesian optimization, a machine-learning-based technique, in order to improve the injection efficiency. During test operations conducted in November-December 2024, this tool successfully enhanced the injection efficiency by up to 32%.

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Phys. Rev. Accel. Beams 29, 105103– Published 7 October, 2026
  • Received 14 October 2025
  • Accepted 1 September 2026
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