Estimating Cosmological Parameters and Reconstructing Hubble Constant with Artificial Neural Networks: A Test with covariance matrix and mock H(z)

Abstract: In this work, we present a new approach to estimate the cosmological parameters and reconstruct the Hubble constant. We reconstructed the function from observational Hub?ble data using an Artificial Neural Network. The training data we used are covariance matrix and mock H(z). With the reconstructed H(z), we can get the Hubble constant, and thus do the comparison with the CMB-based measurements. In order to constrain the cosmological parameters, we sampled data points from the reconstructed data and estimated the posterior distribution. Furthermore, we did many comparisons to test the quality of the reconstructed data. Finally, with the result of the test, we propose that the H(z) reconstructed by our artificial neural network can represent the actual distribution of the real observational data, and can be used in further cosmological research.