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 reconstruct the H(z) based on observational Hubble data with Artificial Neural Network, then estimate the cosmological parameters and the Hubble constant. The training data we used are covariance matrix and mock H(z), which are generated based on the real OHD data and Gaussian Process(GP). The use of the covariance matrix propagates the correlated uncertainties and improves training efficiency. Using the reconstructed H(z) data, we first determine the Hubble constant and compare it with CMB-based measurements. To constrain cosmological parameters, we sample on the reconstructed data and calculate the corresponding posterior distributions with Markov Chain Monte Carlo (MCMC). Through comprehensive statistical comparisons, we demonstrate that the parameter estimation using reconstructed samples achieves comparable statistical accuracy to the result derived from real OHD data.

21 pages, 11 figures. Accepted for publication in the European Physical Journal C

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