## 4 Prospects for Research on the Optimization of MOT Parameters
### Some Suggestions for Parameter Optimization Research  
Parameter optimization is crucial for improving the performance of MOTs and expanding their applications. Some research has already been conducted in this area. Despite the complex structure of MOTs, many research teams have successfully applied parameter optimization in real systems and achieved excellent results. Numerical models have also been advanced to simulate more complex atomic motion states. Furthermore, the variety of optimization methods is increasing, leading to improved computational efficiency and accuracy. Research on optimizing atomic MOT parameters can be further improved in the following areas. (1) It is important to use multiple performance indicators rather than just focusing on the number of atoms to optimize the MOT. Table 3 shows that the capture velocity, atomic beam flux, and atomic group fluorescence are strongly correlated with the number of atoms. Many research teams, with the exception of [PERSON]'s team, primarily used the number of atoms as the ultimate performance indicator for parameter optimization. However, as shown in Table 2, several indicators are used to measure the trapping performance of the MOT, including the temperature, density, and lifetime of the atomic group, as well as the resonance frequency of the trap and the atomic loading time. These performance indicators are equally important for evaluating the performance of MOTs and should be thoroughly studied. (2) Enhancing the research of optimization methods is important. Currently used methods include the control variable method, artificial neural network, and Bayesian optimization. While the control variable method's principle is simple, it takes a long time and requires that each parameter independently affects the result [88]. However, many control parameters of the MOT can interact with each other [83]. Artificial neural networks and Bayesian optimization address the limitations of the control variable method and can determine the optimal control parameter combination more quickly and accurately. The number of applied optimization methods is relatively small at present, and there is a lack of horizontal comparisons between various methods. Therefore, it is necessary to continue exploring optimization methods. (3) Use reference indicators that are easy to measure instead of performance indicators. Many performance indices in MOTs cannot be measured in real time, which presents challenges for parameter optimization. The number of atoms, for example, usually requires artificially finding complete groups of atoms in the image to be accurately measured. However, parameter optimization requires multiple measurements of the number of atoms and feedback under various complex conditions, and it is obvious that other methods are needed to complete the measurement. To overcome this difficulty, [PERSON] used the fluorescence of atomic groups in a fixed region instead of the number of atoms as a performance index to carry out research and ultimately achieved the same effect of increasing the number of atoms [86]. For the other performance indicators listed in Table 2, if there are difficulties in parameter optimization, appropriate reference indicators should also be selected to replace them. (4) Build more accurate numerical models. The working models of atomic MOT parameter optimization include practical systems and numerical models. Compared with the real system, the numerical model has no actual structure, so it is easier to adjust and obtain the control parameters and performance indicators, and it is easier to combine with various optimization methods. However, the accuracy of numerical models is difficult