Seo Sangwon, Lee Jae Hoon, Lee Sang-Bum, Park Sang Eon, Seo Meung Ho, Park Jongcheol, Kwon Taeg Yong, Hong Hyun-Gue
Opt Express. 2021 Oct 25;29(22):35623-35639. doi: 10.1364/OE.437991.
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 - Li, Na, Rb, Sr, Cs, Yb - 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 Rb is experimentally verified using a set of grating chips with different reflectivities and diffraction angles.
我们提出了一套参数集，通过使用机器学习算法在光栅磁光阱（gMOT）中获得最大原子数。在对全局优化构成挑战的多维参数空间中，通过贝叶斯优化并结合蒙特卡罗模拟给出的阱性能评估，有效地对原子数进行了建模。对六种代表性原子种类——锂（Li）、钠（Na）、铷（Rb）、锶（Sr）、铯（Cs）、镱（Yb）——的gMOT进行建模，使我们发现最优光栅反射率始终高于基于平衡光学糖浆的简单估计。我们的算法还得出了与束腰无关的最优衍射角。使用一组具有不同反射率和衍射角的光栅芯片，通过实验验证了铷（Rb）情况下最优参数集的有效性。
