Starlet higher order statistics for galaxy clustering and weak lensing

TL;DR

This paper introduces wavelet-based higher order statistics, specifically starlet peak counts and the $ ext{L}_1$-norm, to enhance constraints in galaxy clustering and weak lensing analyses, demonstrating their potential benefits.

Contribution

It is the first application of starlet peak counts and $ ext{L}_1$-norm to photometric galaxy clustering and weak lensing, showing their effectiveness in improving parameter constraints.

Findings

Starlet peak counts and $ ext{L}_1$-norm improve cosmological parameter constraints.

These statistics outperform the power spectrum in combined probes.

Application demonstrates potential for future weak lensing and galaxy clustering analyses.

Abstract

We present a first application to photometric galaxy clustering and weak lensing of wavelet based multi-scale higher order summary statistics: starlet peak counts and starlet $\ell_1$-norm. Peak counts are the local maxima in the map and the $\ell_1$-norm is computed via the sum of the absolute values of the starlet (wavelet) decomposition coefficients of a map, providing a fast multi-scale calculation of the pixel distribution, encoding the information of all pixels in the map. We employ the cosmo-SLICS simulations sources and lenses catalogues and we compute wavelet based higher order statistics in the context of combined probes and their potential when applied to the weak lensing convergence maps and galaxy maps. We get forecasts on the matter density parameter $\Omega_{\rm m}$, the reduced Hubble constant $h$, the matter fluctuation amplitude $\sigma_8$, and the dark energy equation of state parameter $w_0$. We find that, in our setting for this first application, considering the two probes as independent, starlet peaks and the $\ell_1$-norm represent interesting summary statistics that can improve the constraints with respect to the power spectrum also in the case of photometric galaxy clustering and when the two probes are combined.