https://doi.org/10.1140/epjc/s10052-020-8342-7
Regular Article - Theoretical Physics
Viability tests of f(R)-gravity models with Supernovae Type 1A data
1
Centre for Space Research, North-West University, Potchefstroom, 2520, South Africa
2
Centre for Space Research, North-West University, Mahikeng, 2745, South Africa
* e-mail: renierht@gmail.com
Received:
2
March
2020
Accepted:
10
August
2020
Published online:
28
August
2020
In this work, we will be testing four different general f(R)-gravity models, two of which are the more realistic models (namely the Starobinsky and the Hu–Sawicki models), to determine if they are viable alternative models to pursue a more vigorous constraining test upon them. For the testing of these models, we use 359 low- and intermediate-redshift Supernovae Type 1A data obtained from the SDSS-II/SNLS2 Joint Light-curve Analysis (JLA). We develop a Markov Chain Monte Carlo (MCMC) simulation to find a best-fitting function within reasonable ranges for each f(R)-gravity model, as well as for the Lambda Cold Dark Matter (
CDM) model. For simplicity, we assume a flat universe with a negligible radiation density distribution. Therefore, the only difference between the accepted CDM model and the f(R)-gravity models will be the dark energy term and the arbitrary free parameters. By doing a statistical analysis and using the CDM model as our “true model”, we can obtain an indication whether or not a certain f(R)-gravity model shows promise and requires a more in-depth view in future studies. In our results, we found that the Starobinsky model obtained a larger likelihood function value than the CDM model, while still obtaining the cosmological parameters to be 
for the matter density distribution and 
for the Hubble uncertainty parameter. We also found a reduced Starobinsky model that are able to explain the data, as well as being statistically significant.
© The Author(s), 2020
