基于机器学习的单拍冷原子成像

Single shot imaging for cold atoms based on machine learning

在冷原子研究中,通常采用吸收成像的方式来进行冷原子状态的探测,然而该成像方式受探测过程中光学平面干涉、激光功率、频率、空间位置抖动等方面的扰动,最终形成的冷原子图像总是会出现残余部分空间结构噪声,导致成像质量的下降.尤其是对于冷原子密度稀薄的区域或者飞行时间较久的情况,往往需要大量的重复和平均才能得到理想的信噪比,然而这样不仅导致时间周期大幅度增加,还会引入大量随机噪声.本文基于机器学习提出了一种单拍冷原子成像方案,在该方案中仅需对冷原子进行单次吸收成像,对应背景图片可以通过自动编码器的神经网络来进行生成,有效地降低了成像的空间条纹噪声,大幅度提高成像质量,可以用于冷原子单循环多次成像.

The ability to detect atoms in high spatiotemporal resolution provides a powerful tool for us to investigate the quantum properties of ultracold quantum gases.Plenty of useful imaging methods,including absorption imaging,phase contrast imaging and fluorescence imaging,have been implemented in detecting atoms.Among them,absorption imaging is the most widely used method in cold atoms laboratory.However,the traditional absorption imaging method is affected by perturbations such as interference between optical elements,fluctuation of laser power,frequency,and spatial position,resulting in residual spatially structured noise and degradation of imaging quality.Especially for regions with lower density or for longer time-of-flight,a large number of repetitions are often required to obtain better signal-to-noise ratio,which would increase the time cost and induce other noise.One must reduce the time between two imaging pulses to suppress the spatial noise.A better charge coupled device(CCD) with higher frame transfer rate or other method like fast-kinetic mode will be used to improve the imaging quality.In this paper,a single-shot cold atom imaging method based on machine learning is proposed,in which only one absorption imaging of cold atoms is required,and the corresponding background image can be generated through the neural network of an autoencoder.This effectively reduces the spatial striped noise in imaging,significantly improves the imaging quality,and makes it possible for cold atoms to be imaged multiple times in a single cycle.