Cosmological models discrimination with Weak Lensing

TL;DR

This paper evaluates various non-Gaussian statistical estimators applied to weak lensing data to improve constraints on cosmological parameters and better break degeneracies like sigma_8 and Omega_m.

Contribution

It systematically compares multiple non-Gaussian estimators, introducing Wavelet Peak Counting (WPC) as a highly effective method for cosmological model discrimination.

Findings

Wavelet transform is most sensitive to non-Gaussian structures.

Wavelet Peak Counting (WPC) outperforms other statistics in constraining parameters.

Filtering with MRLens enhances the ability to break parameter degeneracies.

Abstract

Weak gravitational lensing provides a unique method to map directly the dark matter in the Universe. The majority of lensing analyses uses the two-point statistics of the cosmic shear field to constrain the cosmological model yielding degeneracies, such as that between sigma_8 and Omega_M respectively the r.m.s. of the mass fluctuations at a scale of 8 Mpc/h and the matter density parameter both at z = 0. However, the two-point statistics only measure the Gaussian properties of the field and the weak lensing field is non-Gaussian. It has been shown that the estimation of non-Gaussian statistics on weak lensing data can improve the constraints on cosmological parameters. In this paper, we systematically compare a wide range of non-Gaussian estimators in order to determine which one provides tighter constraints on the cosmological parameters. These statistical methods include skewness, kurtosis and the Higher Criticism test in several sparse representations such as wavelet and curvelet; as well as the bispectrum, peak counting and a new introduced statistic called Wavelet Peak Counting (WPC). Comparisons based on sparse representations show that the wavelet transform is the most sensitive to non-Gaussian cosmological structures. It appears also that the best statistic for non-Gaussian characterization in weak lensing mass maps is the WPC. Finally, we show that the sigma_8 -Omega_m degeneracy could be even better broken if the WPC estimation is performed on weak lensing mass maps filtered by the wavelet method, MRLens.