We present an on-line tuning strategy for the ISAC post-accelerator that pre-sets machine optics with a digital twin and then performs Bayesian optimization for steering under online operation with beam. The model computes end-to-end tunes 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, iterations to high transmission tunes are reduced by a factor of 4-6, with final average transmissions in the mid- to high-90% range. By factorizing optics from steering, the dimensionality of the parameter space is reduced, convergence becomes more predictable, and operational safeguards are easier to enforce.
@article{arxiv.2602.20233,
title = {Operational Accelerator Tuning via Model-Coupled Optics and Bayesian Steering},
author = {O. Hassan and O. Shelbaya and P. M. Jung and O. Kester and T. Planche and W. Fedorko},
journal= {arXiv preprint arXiv:2602.20233},
year = {2026}
}