The transverse light-shift can induce non-negligible polarization error in the output signal of spin-exchange relaxation-free (SERF) co-magnetometer. In this paper, a novel method for rapid measurement of transverse light-shift based on the error of steady-state response of co-magnetometer is proposed firstly, then the sources of transverse light-shift in a compact SERF co-magnetometer is modeled and analyzed from three aspects: the non-ideal linear polarization of probe laser, the circular dichroism of the atomic spin ensembles, and the stress-induced birefringence effect of the cell wall. Furthermore, the decoupling and suppression methods of transverse light-shift based on a degree of circular polarization (DOCP) regulation scheme is presented, to realize the decoupling measurement of the transverse light-shift introduced by the whole co-magnetometer cell, and cancel it out with the non-ideal linear polarization of the probe laser. Eventually, the DOCP regulation scheme suggested in this paper achieves more than a 67% suppression ratio in transverse light-shift, and the short- and long-term performance of SERF co-magnetometer are improved due to the reduction of the coupling effect between the probe laser power and transverse field. Moreover, the measurement, decoupling and suppression methods provided in this paper also have the potential to be applied to other atomic sensors, such as the SERF magnetometers and nuclear spin co-magnetometers.
This paper presents a temperature compensation method based on the genetic algorithm (GA) and backpropagation (BP) neural network to reduce the temperature induced error of the spin-exchange relaxation-free (SERF) co-magnetometer. The fluctuation of the cell temperature results in the variation of the optical rotation angle and the probe light absorption. The temperature fluctuation of the magnetic field shielding layer induces the variation of the magnetic field. In addition, one of the causes of light power variation is temperature fluctuation of the optical element. In summary, temperature fluctuations cause a variety of SERF co-magnetometer errors, and the relationship between these errors and temperature fluctuations has the characteristics of time-variance and non-linearity. There are two kinds of methods to suppress these errors. One way is to reduce temperature fluctuations of the SERF co-magnetometer. However, this method requires additional hardware and high cost, which are not suitable for miniaturization and low cost applications. Another effective method to suppress nonlinear and time-varying errors is to utilize intelligent algorithms for temperature compensation. In this paper, the BP neural network is applied for temperature compensation, and the GA is utilized to overcome the disadvantages of the BP neural network. The training data were obtained by changing the ambient temperature of the SERF co-magnetometer. The experimental results show that the method proposed in this work can significantly improve the accuracy of the co-magnetometer at complex ambient temperatures, and the stability of the SERF co-magnetometer at room temperature can be improved by at least 45%.
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