AI 中文总结
本研究将机器学习模拟器与贝叶斯方法结合，通过校准Bmad数字孪生，利用AGS Booster的束流测量数据估计四极磁铁传递函数的乘性参数，提升了加速器数字孪生的模型质量并提供参数与预测的误差范围。
AI 中文摘要
粒子加速器的数字孪生用于规划和控制运行以及设计数据采集方案。精确建模通常需要知晓难以直接测量的量，例如磁铁对准度、将电源电流关联至磁场的传递函数、磁铁非线性效应以及杂散场。本研究中，我们在四极磁铁传递函数中引入乘性参数，以参数化上述效应。我们采用贝叶斯方法，通过将Bmad数字孪生校准至布鲁克海文国家实验室AGS Booster处开展的束流测量，概率性地估计这些参数及其不确定性。借助针对物理加速器数字孪生训练的机器学习模拟器，该模拟器基于Bmad模拟的扰动参数集合训练而成，推理过程得到计算加速。最终得到受数据约束的参数联合后验分布，该分布已考虑束流监测器误差。将参数估计值纳入数字孪生后，模型质量得到显著提升，并为模型参数及预测结果提供了误差棒。
英文摘要
Digital twins of particle accelerators are used to plan and control operations and to design data collection campaigns. Accurate modeling typically requires knowledge of quantities that are hard to measure directly, e.g., magnet alignments, transfer functions relating power supply currents to magnetic fields, magnet nonlinearities, and stray fields. In this work we introduce multiplicative parameters to the quadrupole transfer functions to parametrize these effects. We use Bayesian methods to probabilistically estimate these parameters and their uncertainties by calibrating the Bmad digital twin to beam measurements performed at the AGS Booster at Brookhaven National Laboratory. The inference is computationally accelerated using a machine learning emulator of the physical accelerator digital twin trained to a perturbed-parameter ensemble of Bmad simulations. The result is a joint posterior distribution over the parameters constrained by the data, taking into account beam monitor errors. Incorporating estimates of the parameters into the digital twin is shown to result in a significant improvement in the quality of the model and provides error bars on the model parameters and predictions.
Comments11 pages, 18 figures
