标题： 基于循环神经网络的时变原子磁强计 显示英文标题

标题： Time-dependent atomic magnetometry with a recurrent neural network

摘要： 我们提出使用循环神经网络，从原子集合上的连续光学法拉第旋转测量中估计波动的磁场。 我们表明，编码器-解码器结构的神经网络可以处理测量数据，并学习记录信号与随时间变化的磁场之间的准确映射。 这种方法的性能与卡尔曼滤波器相当，而无需依赖限制其在特定测量和物理系统中应用的理论假设。 显示英文摘要

摘要： We propose to employ a recurrent neural network to estimate a fluctuating magnetic field from continuous optical Faraday rotation measurement on an atomic ensemble. We show that an encoder-decoder architecture neural network can process measurement data and learn an accurate map between recorded signals and the time-dependent magnetic field. The performance of this method is comparable to Kalman filters while it is free of the theory assumptions that restrict their application to particular measurements and physical systems.