No abstract

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%.

The ferrite magnetic shield is widely used in ultra-high-sensitivity atomic sensors because of its low noise characteristics. However, its noise level varies with temperature and affects the performance of the spin-exchange relaxation-free (SERF) co-magnetometer. Therefore, it is necessary to analyze and suppress the thermal magnetic noise. In this paper, the thermal magnetic noise model of a ferrite magnetic shield is established, and the thermal magnetic noise of ferrite is calculated more accurately by testing the low-frequency complex permeability at different temperatures. A temperature suppression method based on the improved heat dissipation efficiency of the ferrite magnetic shield is also proposed. The magnetic noise of the ferrite is reduced by 46.7%. The experiment is basically consistent with the theory. The sensitivity of the co-magnetometer is decreased significantly, from 1.21 ×10−5∘/s/Hz1/2 to 7.02 ×10−6∘/s/Hz1/2 at 1 Hz. The experimental results demonstrate the effectiveness of the proposed method. In addition, the study is also helpful for evaluating the thermal magnetic noise of other materials.

According to the temperature characteristics of SERF co-magnetometer, three temperature compensation methods are proposed in this paper, including particle swarm optimization radial basis function (PSO-RBF) neural network, Gaussian regression and least squares support vector basis. The effectiveness of the three compensation methods is verified by experiments. In order to improve the effect of temperature compensation, this paper also conducts correlation and cluster analysis on the different positions temperature and output signals of the SERF co-magnetometer, and selects the data of temperature points that are closely related to signal bias changes for model training. Through experimental comparison with traditional linear compensation and back-propagation neural network compensation methods, it is found that PSO-RBF neural network has advantages in training speed, compensation accuracy and robustness. Experiments show that PSO-RBF neural network temperature compensation algorithm improves the stability of the SERF co-magnetometer by more than 53% at room temperature or under artificially imposed temperature changes.

The spin-exchange relaxation-free (SERF) co-magnetometers have promising applications in both inertial navigation and fundamental physics experiments. However, the fluctuation in the spin polarization caused by the probe beam has a non-negligible influence on the co-magnetometer signal. In this paper, a theoretical model containing three parameters of the probe beam is established by extending the coupled Bloch equations. Based on this model, the influences of probe power density on the transient and steady-state response of a SERF co-magnetometer are analyzed. According to the transient response model, a new measurement method for transverse optical pumping of the probe beam is proposed. Then for the steady-state response model, a steady-state error suppression method is suggested by adjusting the degree of circular polarization of the probe beam. Eventually, the suppression method is used to refine the SERF co-magnetometer and achieves a suppression rate of 70.31% in transverse electron spin polarization fluctuations, thus improving the co-magnetometer to a stability of 0.0079°/h. In our knowledge, it is better than what has been reported so far.