Reconstruction of the dark sectors’ interaction: A model-independent inference and forecast from GW standard sirens

Abstract

Interacting dark matter (DM) - dark energy (DE) models have been intensively investigated in the literature for their ability to fit various data sets as well as to explain some observational tensions persisting within the ΛCDM cosmology. In this work, we employ the Gaussian processes (GP) algorithm to perform a joint analysis by using the geometrical cosmological probes such as Cosmic chronometers, Supernova Type Ia, Baryon Acoustic Oscillations, and the H0LiCOW lenses sample to infer a reconstruction of the coupling function between the dark components in a general framework, where the DE can assume a dynamical character via its equation of state. In addition to the joint analysis with these data, we simulate a catalog with standard siren events from binary neutron star mergers, within the sensitivity predicted by the Einstein Telescope, to reconstruct the dark sector coupling with more accuracy in a robust way. We find that the particular case, where w = −1 is fixed on the DE nature, has a statistical preference for an interaction in the dark sector at late times. In the general case, where w(z) is analyzed, we find no evidence for such dark coupling, and the predictions are compatible with the ΛCDM paradigm. When the mock events of the standard sirens are considered to improve the kernel in GP predictions, we find a preference for an interaction in the dark sector at late times.

Model-independent reconstruction of the interacting dark energy kernel: Binned and Gaussian process

“…Here we notice the existence of regions where the standard model remains outside the 68% CL (1-σ), which could motivate further studies of an interaction kernel with the presence of oscillations. The general tendency of this function also resembles a previously obtained result in [123] with regards to the predominant negative values at late times. The last reconstructed derived features are the effective equations of state parameters and the energy densities.…”

“…A possible way to avoid these issues it to perform reconstructions by extracting information directly from the data, using model-independent techniques or non-parametric ones, such as Artificial Neural Networks [108][109][110], Gaussian Process [110][111][112][113][114][115][116][117][118] or, recently, we can see applications of binning, linear interpolations and the incorporation of a correlation function in [119]. The Gaussian process (GP), specifically for the IDE models, has become a regular choice for a non-parametric approach [120][121][122][123][124][125]. This methodology has found JCAP11(2023)051 a possibility of an interaction and given some insights into possible preferred behaviors and characteristics, such as a crossing of the non-interacting line.…”

Further understanding the interaction between dark energy and dark matter: current status and future directions

“…Adding GW data could detect the interaction at the 3σ C.L. [353], improve the statistical significance of the null result [356] or reconstruct the coupling function [71].…”

Reconstructing the growth index $$\gamma $$ with Gaussian processes

“…It allows us to study various problems independently of an underlying cosmological model. GP have been used to study the evolution of the dark energy state constant, w(z) [39,[62][63][64], the deceleration parameter, q(z) [40,65], [ f σ 8 ](z) [37,[66][67][68], the homogeneity scale, R H (z) [50], the duality relation, η(z) [69], and a possible time evolution of the growth index, i.e., γ = γ (z) [17,36,37], among several other applications in modern cosmology [70][71][72][73][74][75][76][77][78][79].…”