标题： 使用机器学习优化冷铯原子加载到空心光纤中 显示英文标题

标题： Optimizing loading of cold cesium atoms into a hollow-core fiber using machine learning

摘要： 实验多参数优化可以增强冷原子与波导和腔体的耦合。 机器学习（ML）算法的最新实现展示了在多维参数空间中优化复杂冷原子实验序列的能力。 在这里，我们报告使用机器学习来优化冷原子加载到空心光纤中的过程。 我们使用高斯过程机器学习，在M-LOOP（一个开源在线机器学习接口）中执行此优化。 这是通过根据光学"漂白"的原子计数测量反馈，迭代调整实验参数来实现的。 我们测试了机器学习的效果，同时进行手动扫描，以达到最佳加载条件。 我们调查了多个机器学习运行，以自动访问显著的原子加载条件。 结合实验设计选择，机器学习辅助优化在复杂冷原子实验的实施和维护中具有前景。 显示英文摘要

摘要： Experimental multi-parameter optimization can enhance the interfacing of cold atoms with waveguides and cavities. Recent implementations of machine learning (ML) algorithms demonstrate the optimization of complex cold atom ex perimental sequences in a multi-dimensional parameter space. Here, we report on the use of ML to optimize loading of cold atoms into a hollow-core fiber. We use Gaussian process machine learning in M-LOOP, an open-source online machine learning interface, to perform this optimization. This is implemented by iteratively adjusting experimental parameters based on feedback from an atom-counting measurement of optical "bleaching". We test the effectiveness of ML, alongside a manual scan, to converge to optimal loading conditions. We survey multiple ML runs to auto matically access appreciable atom-loading conditions. In conjunction with experimental design choices, ML-assisted optimization holds promise in the implementation and maintenance of complex cold atom experiments.