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Euclid preparation. LXXXV. Toward a DR1 application of higher-order weak lensing statistics
Authors:
Euclid Collaboration,
S. Vinciguerra,
F. Bouchè,
N. Martinet,
L. Castiblanco,
C. Uhlemann,
S. Pires,
J. Harnois-Déraps,
C. Giocoli,
M. Baldi,
V. F. Cardone,
A. Vadalà,
N. Dagoneau,
L. Linke,
E. Sellentin,
P. L. Taylor,
J. C. Broxterman,
S. Heydenreich,
V. Tinnaneri Sreekanth,
N. Porqueres,
L. Porth,
M. Gatti,
D. Grandón,
A. Barthelemy,
F. Bernardeau
, et al. (262 additional authors not shown)
Abstract:
This is the second paper in the HOWLS (higher-order weak lensing statistics) series exploring the usage of non-Gaussian statistics for cosmology inference within Euclid. With respect to our first paper, we develop a full tomographic analysis based on realistic photometric redshifts that allows us to derive Fisher forecasts in the ($σ_8$, $w_0$) plane for a Euclid-like data release 1 (DR1) setup. W…
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This is the second paper in the HOWLS (higher-order weak lensing statistics) series exploring the usage of non-Gaussian statistics for cosmology inference within Euclid. With respect to our first paper, we develop a full tomographic analysis based on realistic photometric redshifts that allows us to derive Fisher forecasts in the ($σ_8$, $w_0$) plane for a Euclid-like data release 1 (DR1) setup. We find that the five higher-order statistics (HOS) that satisfy the Gaussian likelihood assumption of the Fisher formalism (one-point probability distribution function, $\ell$1-norm, peak counts, Minkowski functionals, and Betti numbers) each outperform the shear two-point correlation functions by a factor of $2.5$ on the $w_0$ forecasts, with only marginal improvement when used in combination with two-point estimators, suggesting that every HOS is able to retrieve both the non-Gaussian and Gaussian information of the matter density field. The similar performance of the different estimators is explained by a homogeneous use of multi-scale and tomographic information, optimized to lower computational costs. These results hold for the three mass mapping techniques of the Euclid pipeline, aperture mass, Kaiser--Squires, and Kaiser--Squires plus, and they are unaffected by the application of realistic star masks. Finally, we explored the use of HOS with the Bernardeau--Nishimichi--Taruya (BNT) nulling scheme approach, finding promising results toward applying physical scale cuts to HOS.
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Submitted 5 January, 2026; v1 submitted 6 October, 2025;
originally announced October 2025.
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Generative modeling of convergence maps based on predicted one-point statistics
Authors:
Vilasini Tinnaneri Sreekanth,
Jean-Luc Starck,
Sandrine Codis
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…
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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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Submitted 2 July, 2025;
originally announced July 2025.
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Theoretical wavelet $\ell_1$-norm from one-point PDF prediction
Authors:
Vilasini Tinnaneri Sreekanth,
Sandrine Codis,
Alexandre Barthelemy,
Jean-Luc Starck
Abstract:
Weak gravitational lensing, resulting from the bending of light due to the presence of matter along the line of sight, is a potent tool for exploring large-scale structures, particularly in quantifying non-Gaussianities. It stands as a pivotal objective for upcoming surveys. In the realm of current and forthcoming full-sky weak-lensing surveys, the convergence maps, representing a line-of-sight in…
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Weak gravitational lensing, resulting from the bending of light due to the presence of matter along the line of sight, is a potent tool for exploring large-scale structures, particularly in quantifying non-Gaussianities. It stands as a pivotal objective for upcoming surveys. In the realm of current and forthcoming full-sky weak-lensing surveys, the convergence maps, representing a line-of-sight integration of the matter density field up to the source redshift, facilitate field-level inference, providing an advantageous avenue for cosmological exploration. Traditional two-point statistics fall short of capturing non-Gaussianities, necessitating the use of higher-order statistics to extract this crucial information. Among the various higher-order statistics available, the wavelet $\ell_1$-norm has proven its efficiency in inferring cosmology (Ajani et al.2021). However, the lack of a robust theoretical framework mandates reliance on simulations, demanding substantial resources and time. Our novel approach introduces a theoretical prediction of the wavelet $\ell_1$-norm for weak lensing convergence maps, grounded in the principles of Large-Deviation theory. We present, for the first time, a theoretical prediction of the wavelet $\ell_1$-norm for convergence maps, derived from the theoretical prediction of their one-point probability distribution. Additionally, we explore the cosmological dependence of this prediction and validate the results on simulations. A comparison of our predicted wavelet $\ell_1$-norm with simulations demonstrates a high level of accuracy in the weakly non-linear regime. Moreover, we show its ability to capture cosmological dependence, paving the way for a more robust and efficient parameter inference process.
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Submitted 14 June, 2024;
originally announced June 2024.