Unleashing cosmic shear information with the tomographic weak lensing PDF

TL;DR

This paper demonstrates that the tomographic weak lensing convergence PDF significantly enhances cosmological parameter constraints, especially when combined with shear two-point functions, by capturing non-Gaussian information in the mildly nonlinear regime.

Contribution

It introduces a method to use the convergence PDF from weak lensing tomography to improve cosmological constraints, validated through simulations and Fisher forecasts.

Findings

Adding tomographic PDF improves parameter constraints by factors of 3.8, 1.3, 1.6.

Joint analysis with shear two-point functions enhances constraints by about 1.5 times.

Convergence PDF retains constraining power even when including additional parameters like Hubble constant.

Abstract

In this work, we demonstrate the constraining power of the tomographic weak lensing convergence PDF for StageIV-like source galaxy redshift bins and shape noise. We focus on scales of $10$ to $20$ arcmin in the mildly nonlinear regime, where the convergence PDF and its changes with cosmological parameters can be predicted theoretically. We model the impact of reconstructing the convergence from the shear field using the well-known Kaiser-Squires formalism. We cross-validate the predicted and the measured convergence PDF derived from convergence maps reconstructed using simulated shear catalogues. Employing a Fisher forecast, we determine the constraining power for $(\Omega_{m},S_{8},w_{0})$. We find that adding a 5-bin tomography improves the $\kappa-$PDF constraints by a factor of $\{3.8,1.3,1.6\}$ for $(\Omega_{m}, S_{8},w_{0})$ respectively. Additionally, we perform a joint analysis with the shear two-point correlation functions, finding an enhancement of around a factor of $1.5$ on all parameters with respect to the two-point statistics alone. These improved constraints come from disentangling $\Omega_{\rm m}$ from $w_0$ by extracting non-Gaussian information, in particular, including the PDF skewness at different redshift bins. We also study the effect of varying the number of parameters to forecast, in particular we add $h$, finding that the convergence PDF maintains its constraining power while the precision from two-point correlations degrades by a factor of $\{1.7,1.4,1.8\}$ for $\{\Omega_{\rm m},S_8,w_0\}$, respectively.