Abstract:
We present in this work a practical general relativity approach in order to create a set of luminosity distance $d_L$ dataset, coming from gravitational wave simulations. Then we use these simulated data, together with type Ia supernova and baryon acoustic oscillations datasets to constrain the parametric space of cosmological models using the Bayesian statistical and Markov Chains Monte Carlo method. We obtained results for five of the studied models, among them, the standard model $\Lambda$CDM, models with variable dark energy equation of state and models with dark sector interaction. One of the models with interaction in the dark sector had conversion problems in the chains and we were not able to constrain the parametric space. In general, the simulations were successful in decreasing the errors of all parameters. We can highlight the values obtained to Hubble constant $H_0 = 69.59 \pm0.59 \ km \ s^{-1} Mpc^{-1}$, matter density parameter $\Omega_{m,0} = 0.3111\pm 0.0095$ and $ w_0 = -1.017 \pm 0.041$ at credibility level $68\%$. Such values are equivalent to a precision of $\varepsilon(H_0) = 0.58\%$, $\varepsilon(\Omega_{m,0}) = 3.05\%$ and $\varepsilon(w_{0}) = 4.03\%$, which is comparable to the accuracy of PLanck 2018. In addition, we also get the dark sector interaction parameter $\xi = 0.0037 \pm 0 .0057$ and $\alpha = 0.229 \pm 0.091$. This shows that with the properly applied gravitational wave simulation technique we can restrict well known models with only late Universe data, thus giving us the opportunity to carry out different types of forecast works, even without data from early Universe. Keywords: Cosmology; Gravitational Waves; Cosmological Models; Dark Sector Interaction; Cosmological Parameters
