Constraints on interacting dark energy models through cosmic chronometers and Gaussian process

Abstract

In this paper, after reconstructing the redshift evolution of the Hubble function by adopting Gaussian process techniques, we estimate the best-fit parameters for some flat Friedmann cosmological models based on a modified Chaplygin gas interacting with dark matter. In fact, the expansion history of the Universe will be investigated because passively evolving galaxies constitute cosmic chronometers. An estimate for the present-day values of the deceleration parameter, adiabatic speed of sound within the dark energy fluid, effective dark energy, and dark matter equation of state parameters is provided. By this, we mean that the interaction term between the two dark fluids, which breaks the Bianchi symmetries, will be interpreted as an effective contribution to the dark matter pressure similarly to the framework of the “Generalized Dark Matter”. We investigate whether the estimates of the Hubble constant and of the present-day abundance of dark matter are sensitive to the dark matter–dark energy coupling. We will also show that the cosmic chronometers data favor a cold dark matter, and that our findings are in agreement with the Le Châtelier–Braun principle according to which dark energy should decay into dark matter.

Gaussian processes and effective field theory of $f(T)$ gravity under the $H_0$ tension

“…Hence, Gaussian processes form a model-independent function reconstruction method without any special physical assumption and parameterization. Therefore, they are widely used in cosmological researches to reconstruct physical parameters from observational data sets [66][67][68][69][70][71][72][73][74][75][76].…”

“…The concept of EFT has been widely applied to cosmological studies [58][59][60][61][62][63], and this approach was developed recently for torsional gravity [64,65]. On the other hand, the Gaussian processes regression provides us a reliable way to obtain fitting functions directly from observational data, and it has been widely used to reconstruct non-linear functions [66][67][68][69][70][71][72][73][74][75][76][77][78][79]. With this approach, we are able to analyse Hubble parameter observational data without any special assumption or specific model.…”

Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data

“…Thus, the optimal values of the hyperparameters are derived from the maximisation of the probability of the GP to generate our considered set of data, that is implemented [88,[91][92][93] via the minimisation of the GP marginal likelihood, which is similar to the hierarchical Bayesian approach. GP have now been exhaustively used for the reconstruction of cosmological functions, particularly related to the late-time cosmic accelerated expansion observables [75,76,88,89,[94][95][96][97][98][99][100][101][102][103][104][105]. We should remark that although GP are independent from any cosmological model, GP rely on the choice of the kernel function which governs the correlations between distinct points in the GP reconstructed function, and hence its profile.…”

A data-driven Reconstruction of Horndeski gravity via the Gaussian processes

“…Most notably, it is a non-parametric way of learning a function and so is a refreshing change of view in making cosmological predictions usually based on arbitrary parametrizations and Markov chain Monte Carlo (MCMC) methods [36][37][38]. In light of the existing tensions between early, i.e., during last scattering, and local cosmological observations, GP has also naturally emerged as a go-to approach in cosmology and is increasingly becoming a popular tool to make cosmological predictions without assuming a particular model of cosmology [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][67][68][69][70][71][72][73].…”