Likelihood-free Cosmological Constraints with Artificial Neural Networks: An Application on Hubble Parameters and SNe Ia

TL;DR

This paper introduces a novel likelihood-free inference method using neural networks, specifically DAE and MAF, to accurately estimate cosmological parameters from complex observational data without relying on explicit likelihood functions.

Contribution

It is the first to combine DAE and MAF neural networks for likelihood-free cosmological inference, improving feature extraction and posterior estimation accuracy.

Findings

Accurately estimates posterior distributions without explicit likelihoods.

Achieves comparable results to traditional MCMC methods.

Demonstrates improved training with DAE for small simulation samples.

Abstract

The errors of cosmological data generated from complex processes, such as the observational Hubble parameter data (OHD) and the Type Ia supernova (SN Ia) data, cannot be accurately modeled by simple analytical probability distributions, e.g. Gaussian distribution. To constrain cosmological parameters from these data, likelihood-free inference is usually used to bypass the direct calculation of the likelihood. In this paper, we propose a new procedure to perform likelihood-free cosmological inference using two artificial neural networks (ANN), the Masked Autoregressive Flow (MAF) and the denoising autoencoder (DAE). Our procedure is the first to use DAE to extract features from data, in order to simplify the structure of MAF needed to estimate the posterior. Tested on simulated Hubble parameter data with a simple Gaussian likelihood, the procedure shows the capability of extracting features from data and estimating posterior distributions without the need of tractable likelihood. We demonstrate that it can accurately approximate the real posterior, achieve performance comparable to the traditional MCMC method, and the MAF gets better training results for small number of simulation when the DAE is added. We also discuss the application of the proposed procedure to OHD and Pantheon SN Ia data, and use them to constrain cosmological parameters from the non-flat $\Lambda$CDM model. For SNe Ia, we use fitted light curve parameters to find constraints on $H_0,\Omega_m,\Omega_\Lambda$ similar to relevant work, using less empirical distributions. In addition, this work is also the first to use Gaussian process in the procedure of OHD simulation.