TL;DR
This paper develops an analytic halo-model based formalism to accurately predict the weak lensing convergence PDF and its covariance, enabling improved cosmological parameter constraints, especially on neutrino mass, from upcoming surveys.
Contribution
The authors introduce the first analytic model for the high-convergence tail of the WL convergence PDF and its covariance, validated against simulations and applied to forecast neutrino mass constraints.
Findings
Model accurately describes the non-Gaussian tail of the convergence PDF
Forecasted neutrino mass constraint of about 0.08 eV from WL PDF alone
Model sensitivity to small-scale systematic effects in simulations
Abstract
The one-point probability distribution function (PDF) is a powerful summary statistic for non-Gaussian cosmological fields, such as the weak lensing (WL) convergence reconstructed from galaxy shapes or cosmic microwave background (CMB) maps. Thus far, no analytic model has been developed that successfully describes the high-convergence tail of the WL convergence PDF for small smoothing scales from first principles. Here, we present a halo-model formalism to compute the WL convergence PDF, building upon our previous results for the thermal Sunyaev-Zel'dovich field. Furthermore, we extend our formalism to analytically compute the covariance matrix of the convergence PDF. Comparisons to numerical simulations generally confirm the validity of our formalism in the non-Gaussian, positive tail of the WL convergence PDF, but also reveal the convergence PDF's strong sensitivity to small-scale…
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