Generative modeling of convergence maps based on predicted one-point statistics
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911157026029568 |
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| author | Sreekanth, Vilasini Tinnaneri Starck, Jean-Luc Codis, Sandrine |
| author_facet | Sreekanth, Vilasini Tinnaneri Starck, Jean-Luc Codis, Sandrine |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative modeling of convergence maps based on predicted one-point statistics Sreekanth, Vilasini Tinnaneri Starck, Jean-Luc Codis, Sandrine Cosmology and Nongalactic Astrophysics 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. |
| title | Generative modeling of convergence maps based on predicted one-point statistics |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2507.01707 |