2014/10/31 by Savvas Nesseris, D. Sapone, Domenico Sapone +2
Mathematics · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Computer science #Cosmology and Gravitation Theories #Dark Matter and Cosmic Phenomena #Data mining #Mathematics #Null (SQL) #Physics #astro-ph.CO #gr-qc
paper · pdf · doi:10.1103/physrevd.91.023004
published as Phys. Rev. D 91, 023004 (2015) · 15 pages; 10 figures; 5 tables
openalex publication_date 2015/01/14 · arxiv created 2015/01/15 · arxiv updated 2015/01/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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\ensuremathσ8 and H(z) or type Ia supernovae data. We perform the reconstruction in two different ways, either by directly binning thedata 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 Lemaitre-Tolman-Bondi 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\ensuremathσ level.