Effect of low anisotropy on cosmological models by using supernova data

TL;DR

This study investigates how low anisotropy affects the evolution of the universe using supernova data, comparing various cosmological models and finding that anisotropy influences best-fit parameters.

Contribution

It introduces an analysis of low anisotropy effects on cosmological models using supernova data, highlighting its impact on parameter fitting and model comparison.

Findings

Anisotropy leads to more best-fit parameters in most models.

The linear parametrization model best fits the data considering anisotropy.

The $ m \Lambda$CDM model has a best fit with $ m \Omega_{\sigma_0}= 0.013$.

Abstract

By using the supernovae type Ia data we study influence of the anisotropy (although low) on the evolution of the universe and compare ${\Lambda}$CDM model with 6 representative parametrizations of the recent Hubble expansion history $H(z)$. To compare these models we use the the maximum likelihood method for find that the best fit dynamical $\omega(z)$ and $q(z)$ obtained from the SNIa dataset. In particular we find the best fit value of $\Lambda$CDM model $(\Omega_{\sigma_0}= 0.013$, $\chi^2_{min}=197.56)$. The analysis shows that by considering the anisotropy, it leads to more best fit parameters in all models (except of SCDM) with SNIa data. We also use two statistical tests such as the usual $\chi^2_{min}/dof$ and p-test to compare different dark energy models. According to both statistical tests and considering anisotropy, the linear parametrization model that is providing the best fit to $\Lambda$CDM model.