2001/01/10 by Licia Verde, Alan Heavens, Alan F. Heavens
Mathematics · Physics and Astronomy · #Astrophysics #Cosmology #Cosmology and Gravitation Theories #Dark energy #Galaxies: Formation, Evolution, Phenomena #Gaussian #Gaussian function #Gaussian random field #Mathematics #Non-Gaussianity #Observable #Parameter space #Physics #Quantum mechanics #Random field #Scientific Research and Discoveries #Spectral density #Statistical physics #Statistics #Trispectrum #astro-ph
paper · pdf · doi:10.1086/320656
to appear in ApJ, 28 pages, 5 figures
arxiv created 2001/01/10 · openalex publication_date 2001/05/20 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In the standard model for structure formation, bound objects originate from the gravitational collapse of small perturbations arising from quantum fluctuations with random phases. In other scenarios, based on defects, structures are seeded by localized energy density. In principle, it is possible to differentiate between these models on the basis of their statistical properties; only in the former case is the initial density field an almost-perfect random Gaussian field. In this paper, we investigate the use of the trispectrum of the galaxy density field, which is the connected four-point function in Fourier space, as a discriminant between Gaussian and non-Gaussian models. It has the advantage of having only weak nonlinear growth. We define a related statistic τ which, as a test of the Gaussian hypothesis, is independent of cosmology, the power spectrum, and biasing, in real space, and which is, in principle, a measure of the departure from Gaussian statistics. For galaxy redshift surveys, the statistic depends on cosmology and bias only through the potentially observable parameter β. We compute the expected errors on the estimate of τ, and demonstrate with numerical simulations that it can be a useful discriminant of models, with the important proviso that any bias is linear on large scales. Whether it is the most effective method is uncertain and depends on the nature of the departure from Gaussianity.