Viability tests of f(R)-gravity models with Supernovae Type 1A data

Abstract

AbstractIn 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 ($$\varLambda $$ΛCDM) model. For simplicity, we assume a flat universe with a negligible radiation density distribution. Therefore, the only difference between the accepted $$\varLambda $$ΛCDM 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 $$\varLambda $$ΛCDM 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 $$\varLambda $$ΛCDM model, while still obtaining the cosmological parameters to be $$\varOmega _{m} = 0.268^{+0.027}_{-0.024}$$Ωm=0.268-0.024+0.027 for the matter density distribution and $${\bar{h}} = 0.690^{+0.005}_{-0.005}$$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.