CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators

Understand

In previous works, we proposed to estimate cosmological parameters with the artificial neural network (ANN) and the mixture density network (MDN).

• In this work, we propose an improved method called the mixture neural network (MNN) to achieve parameter estimation by combining ANN and MDN, which can overcome shortcomings of the ANN and MDN methods.
• Besides, we propose sampling parameters in a hyper-ellipsoid for the generation of the training set, which makes the parameter estimation more efficient.
• A high-fidelity posterior distribution can be obtained using $\mathcal{O}(10^2)$ forward simulation samples.