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arXiv · 2507.01707

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

Vilasini Tinnaneri Sreekanth·Jean-Luc Starck·Sandrine Codis
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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.

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Vilasini Tinnaneri Sreekanth, Jean-Luc Starck, Sandrine Codis. 2025-07-02. Generative modeling of convergence maps based on predicted one-point statistics. https://doi.org/10.1051/0004-6361%2F202554142

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