Generative modeling of convergence maps based on predicted one-point statistics

TL;DR

This paper introduces an efficient emulator for generating convergence maps in weak gravitational lensing, capturing non-Gaussian features by matching wavelet-based statistics without heavy simulations.

Contribution

It presents a novel wavelet-based method to produce accurate convergence maps directly from power spectra and one-point statistics, improving analysis efficiency.

Findings

Maps reproduce input power spectra accurately

Maps exhibit higher-order statistical properties consistent with predictions

Method reduces computational cost compared to simulations

Abstract

Context: Weak gravitational lensing is a key cosmological probe for current and future large-scale surveys. While power spectra are commonly used for analyses, they fail to capture non-Gaussian information from nonlinear structure formation, necessitating higher-order statistics and methods for efficient map generation. Aims: To develop an emulator that generates accurate convergence maps directly from an input power spectrum and wavelet l1-norm without relying on computationally intensive simulations. Methods: We use either numerical or theoretical predictions to construct convergence maps by iteratively adjusting wavelet coefficients to match target marginal distributions and their inter-scale dependencies, incorporating higher-order statistical information. Results: The resulting kappa maps accurately reproduce the input power spectrum and exhibit higher-order statistical properties consistent with the input predictions, providing an efficient tool for weak lensing analyses.