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Accelerator tuning via model-coupled optics and Bayesian steering

O. Hassan1,2,*, O. Shelbaya1,2,*,†, P. M. Jung1,2, O. Kester1,2, T. Planche1,2, and W. Fedorko1

  • 1TRIUMF, 4004 Wesbrook Mall, Vancouver, British Columbia, V6T 2A3, Canada
  • 2Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia, V8W 2Y2, Canada

  • *These authors contributed equally to this work.
  • †Contact author: oshelb@triumf.ca
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Phys. Rev. Accel. Beams 29, 105102 – Published 7 October, 2026

DOI: https://doi.org/10.1103/1vy9-r7c6

Abstract

We present an on-line tuning strategy for the isotope separator and accelerator postaccelerator that presets machine optics with a digital twin and then performs Bayesian optimization for steering during operation with beam. The model computes end-to-end configurations in seconds and interfaces with the control system under device bounds, slew-rate limits, and loss interlocks. We report three experimental case studies demonstrating that decoupling optics from steering yields faster and more reliable convergence than a fully Bayesian optics-plus-steering baseline under identical conditions. Across these cases, the number of iterations required to reach high-transmission solutions was substantially reduced, with final average transmissions in the mid- to high-90% range. By removing optics from the steering optimization, the dimensionality of the parameter space is reduced, convergence becomes more predictable, and operational safeguards are easier to enforce.

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Phys. Rev. Accel. Beams 29, 105102– Published 7 October, 2026
  • Received 19 February 2026
  • Accepted 1 September 2026
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