## 4 Prospects for Research on the Optimization of MOT Parameters  
### Research Progress  
The parameter optimization of the MOT involves a thorough analysis of how the MOT operates. This analysis evaluates how each control parameter affects the trapping performance. The optimal combination of control parameters is then applied to the actual device to improve the trapping performance of the MOT. Currently, there is limited research on optimizing MOT parameters for cold atom interferometers. The existing studies on parameter optimization are presented in chronological order. The molecular MOTs operate similarly to the atomic MOTs and are discussed in conjunction. In 2006, [PERSON] analyzed the variation trend of the atomic number with cooling laser intensity, cooling laser detuning, repumping laser intensity, and magnetic field gradient for Rb atomic MOTs. Some of the test results were in agreement with the results predicted by the two-level system model, which initiated the preliminary exploration of the parameter optimization of MOTs [80]. In 2016, [PERSON] analyzed the one-dimensional atomic motion equation in the MOT of mercury atoms. [PERSON] numerically simulated the relationships between the atomic number in a three-dimensional MOT and the laser intensity, laser detuning, and magnetic field gradient via the random number method, thus further improving the accuracy of the model [36]. From 2017 to 2019, [PERSON] analyzed the effects of pushing a laser, magnetic field gradient, geomagnetic field, cooling laser power, and cooling laser detuning on the steady-state atomic number with the help of a specific test device. [PERSON] gave the influence rule of each parameter and finally increased the steady-state atomic number trapped by the MOT of Rb atoms by an order of magnitude, achieving a good optimization effect [81]. In 2018, [PERSON] introduced an artificial neural network into the control feedback loop to complete parameter optimization in the actual MOT system. The optimized system performance was better than that of the most well-known solutions and generated a higher optical density [82]. In 2021, [PERSON] proposed a multiparameter autonomous optimization system based on an artificial neural network and applied it to the atomic motion model of MOT constructed via the Monte Carlo simulation method. Four parameters, including the cooling laser power, cooling laser detuning, magnetic field gradient, and axial velocity of the atomic beam from the two-dimensional MOT, were optimized with the atomic number as the ultimate goal. Reliable and stable optimum test results were obtained [83]. Additionally, in 2021, [PERSON] et al. from Leibniz University of Hanover, Germany, focused on the trapping rate of CaF molecular MOT as the main goal. They comprehensively considered a parameter set composed of 14 parameters in the polarization state of the cooled laser, the intensity of the cooled laser, the cooling laser detuning, the magnetic field gradient, and the cooling laser radius under the condition of a certain total laser power. The maximum trapping velocity and the corresponding parameter combination were found via \begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Performance Indicator Name** & **Unit** \\ \hline
1 & Atomic number & - \\
2 & Atomic density & - \\
3 & Atomic temperature & K \\
4 & Atomic lifetime & s \\
5 & Volume & m\({}^{3}\) \\
6 & Resonant frequency & Hz \\
7 & Atomic loading time & s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MOT performance indices. the Bayes optimization tool. There is also a certain logarithmic relationship between the trapping rate and the laser power [84]. In 2023, [PERSON] used the Runge-Kutta method to solve the equation of atomic motion and simulated the working process of a two-dimensional MOT via the Monte Carlo method.