Cosmology with photometric weak lensing surveys: constraints with redshift tomography of convergence peaks and moments

TL;DR

This paper forecasts how combining redshift tomography, peak counts, and higher moments in weak lensing surveys can significantly improve constraints on cosmological parameters, especially when combined with CMB priors.

Contribution

It demonstrates the substantial gains in cosmological parameter constraints achievable through combined tomography, non-Gaussian statistics, and CMB priors in photometric weak lensing surveys.

Findings

Redshift tomography reduces parameter uncertainty by a factor of 8.

Adding non-Gaussian statistics further reduces uncertainties by factors of 3 and 4.

Combining all methods with CMB priors improves constraints by nearly a factor of 9.

Abstract

Weak gravitational lensing is becoming a mature technique for constraining cosmological parameters, and future surveys will be able to constrain the dark energy equation of state $w$. When analyzing galaxy surveys, redshift information has proven to be a valuable addition to angular shear correlations. We forecast parameter constraints on the triplet $(\Omega_m,w,\sigma_8)$ for an LSST-like photometric galaxy survey, using tomography of the shear-shear power spectrum, convergence peak counts and higher convergence moments. We find that redshift tomography with the power spectrum reduces the area of the $1\sigma$ confidence interval in $(\Omega_m,w)$ space by a factor of 8 with respect to the case of the single highest redshift bin. We also find that adding non-Gaussian information from the peak counts and higher-order moments of the convergence field and its spatial derivatives further reduces the constrained area in $(\Omega_m,w)$ by a factor of 3 and 4, respectively. When we add cosmic microwave background parameter priors from Planck to our analysis, tomography improves power spectrum constraints by a factor of 3. Adding moments yields an improvement by an additional factor of 2, and adding both moments and peaks improves by almost a factor of 3, over power spectrum tomography alone. We evaluate the effect of uncorrected systematic photometric redshift errors on the parameter constraints. We find that different statistics lead to different bias directions in parameter space, suggesting the possibility of eliminating this bias via self-calibration.