通过模型耦合光学与贝叶斯引导实现操作加速器调谐
Operational Accelerator Tuning via Model-Coupled Optics and Bayesian Steering
AI总结：
本文提出了一种基于模型耦合光学与贝叶斯引导的在线调谐策略，通过解耦光学与引导提升加速器调谐的效率与可靠性。
摘要：
我们提出了一种在线调谐策略，用于ISAC后加速器，该策略利用数字双预设机器光学，并在在线操作中通过贝叶斯优化进行引导。模型能够在数秒内计算端到端的调谐参数，并与受设备限制、 slew-rate 限制和损失互锁的控制系统接口。我们报告了三个实验案例研究，证明将光学与引导解耦比在相同条件下使用完全贝叶斯光学加引导基线方法能更快且更可靠地收敛。在这些案例中，达到高传输调谐的迭代次数减少了4-6倍，最终平均传输率在中等到高90%范围内。通过将光学与引导分解，参数空间的维度减少，收敛变得更具可预测性，操作安全措施也更容易实施。
英文摘要：
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.
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