Information Content in the Angular Power Spectrum of Weak Lensing: Wavelet Method

TL;DR

This paper introduces a wavelet-based non-linear Wiener filter that effectively recovers Fisher information from weak lensing data, significantly enhancing the potential for constraining cosmological parameters like dark energy.

Contribution

The paper presents a novel wavelet-based filtering method that outperforms existing techniques in recovering Fisher information from weak lensing convergence fields.

Findings

Recovers about four times more Fisher information than unfiltered data.

Outperforms logarithmic mapping, recovering up to three times more information.

Potentially improves the constraining power of weak lensing surveys on cosmological parameters.

Abstract

We quantify the performance of a non-linear Wiener filter, constructed in wavelet space, at recovering some of the Fisher information that was lost in the weak lensing convergence field. The proposed method consists in a separation of the original field into the sum of a Gaussian and a non-Gaussian contribution. After filtering an ensemble of such fields, which are obtained from $N$-body simulations, we find that we can recapture about four times more Fisher information, an effect that can potentially improve by a significant amount the constraining power of weak lensing surveys on cosmological parameters, including the dark energy equation of state $\omega$. We compare this performance with that of the logarithmic mapping and find that the wavelet method can recover up to three times more information.