AI 中文总结
本文提出利用GPTune框架的贝叶斯优化方法，通过控制束腰位置$s^*$来优化RHIC的sPHENIX亮度，在实验中成功识别局部最大值并恢复劣化配置，展示了其在噪声环境下实时调谐的鲁棒性，为下一代对撞机提供了有力工具。
AI 中文摘要
在相互作用点（IP）处最大化亮度要求碰撞位置 $s_{IP}$ 与最小β函数的纵向位置 $s^*$ 重合。因此，精确的光学测量和对 $s^*$ 的控制对于亮度优化至关重要。在相对论重离子对撞机（RHIC）上，运行中的相互作用点之间的平均水平β拍测量值约为 $20%$，且测得的 $s^*$ 存在显著变化。对于具有短束团长度和大交叉角度的现代高能对撞机，精确控制束腰位置 $s^*$ 尤其具有挑战性。我们提出了一种使用GPTune框架的在线贝叶斯优化（BO）应用，通过RHIC处的 $s^*$ 控制来优化sPHENIX亮度。GPTune首先在RHIC电子束离子源（EBIS）上进行了验证，在那里它相对于基线实现了高达 $70%$ 的束流强度提升，尽管经验丰富的操作员通过更长时间的手动调节也能达到类似的性能。该框架随后在sPHENIX运行期间部署。由于实时sPHENIX MVTX信号不可用，使用强度归一化的零度量热计（ZDC）信号作为优化目标，GPTune识别出了局部亮度最大值，从故意劣化的 $s^*$ 配置中恢复，并揭示了相互作用区域中残余的水平与垂直束腰偏移。这些结果证明了贝叶斯优化在噪声、时变条件下进行实时对撞机调节的鲁棒性和效率。$s^*$ 控制方法为精确亮度优化提供了一种有前景的工具，并且特别适用于下一代短束团对撞机，如电子-离子对撞机（EIC）。
英文摘要
Maximizing luminosity at the interaction point (IP) requires the collision location $s_{IP}$ to coincide with the longitudinal position of the minimum beta function, $s^$. Accurate optics measurements and control of $s^$ are therefore essential for luminosity optimization. At the Relativistic Heavy Ion Collider (RHIC), average horizontal beta-beat measurements between operating IPs are approximately $20%$, with significant variation in measured $s^*$. Precise control of the beam waist position $s^$ is particularly challenging for modern high-energy colliders with short bunch lengths and large crossing angles. We present an online Bayesian optimization (BO) application using the GPTune framework to optimize sPHENIX luminosity through $s^$ control at RHIC. GPTune was first validated at the RHIC Electron Beam Ion Source (EBIS), where it achieved up to a $70%$ increase in beam intensity over the baseline, although experienced operators could reach similar performance with longer manual tuning. The framework was subsequently deployed during sPHENIX operations. Using an intensity-normalized Zero-Degree Calorimeter (ZDC) signal as the optimization objective, due to the unavailability of the live sPHENIX MVTX signal, GPTune identified local luminosity maxima, recovered from intentionally degraded $s^$ configurations, and revealed residual horizontal and vertical waist offsets in the interaction region. These results demonstrate the robustness and efficiency of Bayesian optimization for real-time collider tuning under noisy, time-varying conditions. The $s^$ control methodology provides a promising tool for precision luminosity optimization and is particularly relevant to next-generation short-bunch colliders such as the Electron-Ion Collider (EIC).
