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    Consistency test of the large-scale structure with Bernardeau-Nishimichi-Taruya transform of the KiDS-Legacy survey

    Shiming Gu (顾时铭)1,2,*, Ziang Yan (颜子昂)2,3,4,†, Ludovic van Waerbeke1,‡, Francis Bernardeau5,6, Hendrik Hildebrandt2, Angus H. Wright2, Maciej Bilicki7, Christos Georgiou8, Shun-Sheng Li (李顺生)9,10 et al.

    Laila Linke11, Lauro Moscardini12,13,14, Robert Reischke15,2, and Benjamin Stölzner2

    • *Contact author: gsm@phas.ubc.ca
    • †Contact author: yan.ziang.h1@f.mail.nagoya-u.ac.jp
    • ‡Contact author: waerbeke@phas.ubc.ca

    Phys. Rev. D 114, 083535 – Published 23 October, 2026

    DOI: https://doi.org/10.1103/lfs2-d8xd

    Abstract

    We perform the first k-cut cosmic shear analysis of the KiDS-Legacy survey. This method uses the Bernardeau-Nishimichi-Taruya (BNT) transform to construct weak-lensing kernels that are more localized than conventional ones, and remove information from selected physical scales while retaining the constraining power of the targeted range. Removing the scale of k≥0.33  Mpc−1 from the KiDS-Legacy pseudo-Cℓ data vector, and using a covariance matrix whose Gaussian component is computed from the theoretical data vector, we find S8=0.798−0.045+0.045. This agrees with both the fiducial KiDS-Legacy bandpower result and our no-k-cut pseudo-Cℓ posterior to within 0.1σ, indicating no significant bias from nonlinear astrophysical feedback at the precision of KiDS-Legacy. We also study the case in which the Gaussian covariance is computed from the observed data vector. In this setup, the same scale cut of k<0.33  Mpc−1 gives a much lower S8=0.717−0.046+0.047. Further k-cut tests reveal a mild scale-dependent trend, with larger physical scales preferring lower S8 values and a maximum low- versus high-k deviation of 1.80σ. Mock tests show that this behavior is not produced by the covariance prescription or data vector alone, but may arise from their interplay. These results show that BNT k-cuts provide both a mitigation strategy for nonlinear systematics and a diagnostic of weak-lensing inference pipelines.

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