Viability tests of $\mathit{\text{f(R)}}$-gravity models with Supernovae Type 1A data

In this work, we will be testing four different general $\mathit{\text{f(R)}}$-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 thRede 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 $\mathit{\text{f(R)}}$-gravity model, as well as for the Lambda Cold Dark Matter ($\mathrm{\Lambda }$CDM) model. For simplicity, we assume a flat universe with a negligible radiation density distribution. Therefore, the only difference between the accepted $\mathrm{\Lambda }$CDM model and the $\mathit{\text{f(R)}}$-gravity models will be the dark energy term and the arbitrary free parameters. By doing a statistical analysis and using the $\mathrm{\Lambda }$CDM model as our “true model”, we can obtain an indication whether or not a certain $\mathit{\text{f(R)}}$-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 $\mathrm{\Lambda }$CDM model, while still obtaining the cosmological parameters to be $\Omega_{m} = 0.268^{+0.027}{-0.024}$forthematterdensitydistributionand$\bar{h} = 0.690^{+0.005}{-0.005}$ 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.