Reconstruction of the null-test for the matter density perturbations

TL;DR

This paper evaluates the null-test for matter density perturbations using current and future growth rate data, demonstrating its potential to distinguish between various dark energy and modified gravity models with high confidence.

Contribution

It systematically reconstructs the null-test using multiple data sets and methods, and assesses its discriminative power with simulated future data across different cosmological models.

Findings

Null-test can effectively differentiate models with future data.

Reconstruction accuracy depends on data quality and method used.

Future surveys like LSST will enable high-confidence model discrimination.

Abstract

We systematically study the null-test for the growth rate data first presented in [S. Nesseris and D. Sapone, arXiv:1409.3697] and we reconstruct it using various combinations of data sets, such as the $f\sigma_8$ and $H(z)$ or Type Ia supernovae (SnIa) data. We perform the reconstruction in two different ways, either by directly binning the data or by fitting various dark energy models. We also examine how well the null-test can be reconstructed by future data by creating mock catalogs based on the cosmological constant model, a model with strong dark energy perturbations, the $f(R)$ and $f(G)$ models, and the large void LTB model that exhibit different evolution of the matter perturbations. We find that with future data similar to an LSST-like survey, the null-test will be able to successfully discriminate between these different cases at the $5\sigma$ level.