Density Fluctuation Reconstruction using KS test and D'Agostino's K-squared test

TL;DR

This paper introduces a novel non-parametric approach using KS and D'Agostino's K-squared tests to reconstruct density fluctuations and constrain cosmological models directly from observational data, bypassing traditional power spectrum methods.

Contribution

The paper presents a new method applying statistical tests to density fluctuations for cosmological constraints, offering an alternative to power spectrum-based techniques.

Findings

Method successfully recovers Gaussian distribution of density fluctuations.

Verified with Gaussian random fields and N-body simulations.

Potential to distinguish initial density fluctuations from growth factors.

Abstract

We propose a new non-parametric method to constrain the cosmological model through the growth factor of large-scale structure. To constrain the cosmological model from observations such as cosmic microwave background or large-scale structure, the power spectrum or correlation function have been used since they are well described by the linear perturbation theory or little correction is needed for the non-linearity; however, it is always important to find an alternative route to constrain cosmological models in order to avoid systematic effects due to the estimator and make the constraint more robust.Our density fluctuation reconstruction method constrains the cosmological model directly from time evolution of density fluctuation. We propose to apply Kolmogorov-Smirnov test and D'Agostino's K-squared test to the distribution function of density fluctuations, which are observed on a light cone. The density fluctuation recovers the Gaussian distribution only if we reconstruct the density fluctuation with correct linear growth factors. The method is verified by the use of Gaussian random field and N-body simulations. This method will open a new window both to separate the initial three dimensional density fluctuation and the growth factor of large scale structure.