Time-dependent atomic magnetometry with a recurrent neural network

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