Maximized atom number for a grating magneto-optical trap via machine-learning assisted parameter optimization.
Maximized atom number for a grating magneto-optical trap via machine-learning assisted parameter optimization.
复制标题
通过机器学习辅助参数优化，最大化光栅磁光陷阱的原子数。
作者：
Sangwon Seo;Jae Hoon Lee;Sang;S. Park;Meung Ho Seo;Jongcheol Park;T. Kwon;Hyun
我们提出了一个参数设置，以获得最大数量的原子在光栅磁光阱（gMOT）通过采用机器学习算法。在多维参数空间中，这对全局优化提出了挑战，原子数是有效地建模通过贝叶斯优化与蒙特-卡罗模拟给出的陷阱性能的评估。对六种代表性原子种类--7 Li、23 Na、87 Rb、88 Sr、133 Cs、174 Yb--的gMOT进行建模，使我们能够发现，最佳光栅反射率始终高于基于平衡光学糖蜜的简单估计。我们的算法还产生了最佳衍射角，这是独立的束腰。利用一组具有不同反射率和衍射角的光栅芯片对87 Rb的最佳参数集进行了实验验证。
We present a parameter set for obtaining the maximum number of atoms in a grating magneto-optical trap (gMOT) by employing a machine learning algorithm. In the multi-dimensional parameter space, which imposes a challenge for global optimization, the atom number is efficiently modeled via Bayesian optimization with the evaluation of the trap performance given by a Monte-Carlo simulation. Modeling gMOTs for six representative atomic species - 7Li, 23Na, 87Rb, 88Sr, 133Cs, 174Yb - allows us to discover that the optimal grating reflectivity is consistently higher than a simple estimation based on balanced optical molasses. Our algorithm also yields the optimal diffraction angle which is independent of the beam waist. The validity of the optimal parameter set for the case of 87Rb is experimentally verified using a set of grating chips with different reflectivities and diffraction angles.