Making the leap I: Modelling the reconstructed lensing convergence PDF from cosmic shear with survey masks and systematics

TL;DR

This paper develops a method to accurately model the probability distribution of reconstructed lensing convergence in cosmic shear data, accounting for survey masks and systematics, advancing the application of large deviations theory in cosmology.

Contribution

It introduces a novel approach to model the reconstructed convergence PDF with survey masks and systematics using large deviations theory, enabling more precise cosmological analyses.

Findings

Accurate modeling of the convergence PDF under realistic survey conditions.

Incorporation of systematics like intrinsic alignments, shear biases, and photo-z errors.

Framework sets the stage for robust likelihood analyses in future work.

Abstract

The last few years have seen the development of a promising theoretical framework for statistics of the cosmic large-scale structure -- the theory of large deviations (LDT) for modelling weak-lensing one-point statistics in the mildly non-linear regime. The goal of this series of papers is to make the leap and lay out the steps to perform an actual data analysis with this theoretical tool. Building upon the LDT framework, in this work (Paper I) we demonstrate how to accurately model the Probability Distribution Function (PDF) of a reconstructed Kaiser-Squires convergence field under a realistic mask, that of the third data release of the Dark Energy Survey (DES). We also present how weak lensing systematics and higher-order lensing corrections due to intrinsic alignments, shear biases, photo-$z$ errors and baryonic feedback can be incorporated in the modelling of the reconstructed convergence PDF. In an upcoming work (Paper II) we will then demonstrate the robustness of our modelling through simulated likelihood analyses, the final step required before applying our method to actual data.