The Uncertainty Quantification for Parameters Optimization in SERF Atomic Magnetomer

The magnetometer is a multi-parameter system, parameters optimization is always the problem to improve the sensitivity of the atomic magnetometer. Multiple parameters hardly decide the optimized conditions in the experiments because of the influence of several parameters and parameters may interact with each other. Besides, the influence of parameters on the signal-to-noise ratio can hardly be analyzed theoretically due to the complex physical configuration. We used a generalized polynomial chaos expansion to construct an agent model for the SERF atomic magnetometer to replace its complex physical model, and used a variance-based sobol method to perform a global sensitivity analysis of the parameters in this paper. The mathematical method of uncertainty quantification is used to comprehensively analyze six parameters’ effect on SERF atomic magnetometer performance. The results show that the probe laser power has the greatest impact on the atomic magnetometer, and it is the parameter with the highest degree of linearity with the atomic magnetometer. In accordance with the results of the uncertainty quantification analysis, the parameters of the probe laser power of the SERF atomic magnetometer were experimentally optimized, and the performance of the magnetometer was significantly improved.