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Lookup NU author(s): Dr Lina Castiblanco Tolosa, Professor Cora Uhlemann, Dr Joachim Harnois-DerapsORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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 (σ8, w0) 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, ℓ1-norm, peak counts, Minkowski functionals, and Betti numbers) each outperform the shear two-point correlation functions by a factor of 2.5 on the w0 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.
Author(s): Vinciguerra S, Bouchè F, Martinet N, Castiblanco L, Uhlemann C, Pires S, Harnois-Déraps J, et al
Publication type: Article
Publication status: Published
Journal: Astronomy and Astrophysics
Year: 2026
Volume: 707
Online publication date: 17/03/2026
Acceptance date: 15/12/2025
Date deposited: 08/04/2026
ISSN (print): 0004-6361
ISSN (electronic): 1432-0746
Publisher: EDP Sciences
URL: https://doi.org/10.1051/0004-6361/202557573
DOI: 10.1051/0004-6361/202557573
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