| 报告摘要： |
In this talk, I will present how to constrain cosmological parameters with Neural Density Estimators (NDEs). Specifically, I will show three NDEs that we presented: the artificial neural network (ANN), the mixture density network (MDN), and the mixture neural network (MNN). I will show the principle of estimating parameters using these three NDEs. Then I will show a flexible code called CoLFI that we developed to constrain parameters using ANN, MDN, and MNN, which is suitable for any parameter estimation of complicated models in a wide range of scientific fields. CoLFI can obtain a high-fidelity posterior distribution using O(10^2) simulation samples, which makes parameter estimation faster, especially for complex and resource-consuming cosmological models. In addition, I will briefly introduce the 21 cm related research I participated in, and look forward to the application prospects of CoLFI and machine learning methods in 21cm cosmology. |
