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Peer-reviewedCC BY 4.0Phys.Rev.D

KiDS-Legacy: The consistency test of the large-scale structure with Bernardeau-Nishimichi-Taruya transform

  • Shiming Gu
  • Ziang Yan
  • Ludovic van Waerbeke
  • Francis Bernardeau
  • Hendrik Hildebrandt
  • Angus H Wright
  • Maciej Bilicki
  • Christos Georgiou
  • Shun-Sheng Li
  • Laila Linke
  • Lauro Moscardini
  • Robert Reischke
1 more authors
  • Benjamin Stölzner
Open at sourcePDFDOICite

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 localised 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 behaviour 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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Cite

BibTeXRIS
BibTeX
@article{gu2026kidslegacy,
  title = {{KiDS-Legacy: The consistency test of the large-scale structure with Bernardeau-Nishimichi-Taruya transform}},
  author = {Shiming Gu and Ziang Yan and Ludovic van Waerbeke and Francis Bernardeau and Hendrik Hildebrandt and Angus H Wright and Maciej Bilicki and Christos Georgiou and Shun-Sheng Li and Laila Linke and Lauro Moscardini and Robert Reischke and Benjamin Stölzner},
  journal = {Phys.Rev.D},
  year = {2026},
  month = sep,
  doi = {10.1103/lfs2-d8xd},
  eprint = {2607.04384},
  archivePrefix = {arXiv},
  url = {https://hal.science/hal-05734226v1},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - KiDS-Legacy: The consistency test of the large-scale structure with Bernardeau-Nishimichi-Taruya transform
AU  - Shiming Gu
AU  - Ziang Yan
AU  - Ludovic van Waerbeke
AU  - Francis Bernardeau
AU  - Hendrik Hildebrandt
AU  - Angus H Wright
AU  - Maciej Bilicki
AU  - Christos Georgiou
AU  - Shun-Sheng Li
AU  - Laila Linke
AU  - Lauro Moscardini
AU  - Robert Reischke
AU  - Benjamin Stölzner
PY  - 2026
DA  - 2026/09/01
JO  - Phys.Rev.D
DO  - 10.1103/lfs2-d8xd
UR  - https://hal.science/hal-05734226v1
AB  - 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 localised 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-C_ℓ data vector, and using a covariance matrix whose Gaussian component is computed from the theoretical data vector, we find S_8 = 0.798_−0.045^+0.045. This agrees with both the fiducial KiDS-Legacy bandpower result and our no-k-cut pseudo-C_ℓ 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 S_8=0.717_−0.046^+0.047. Further k-cut tests reveal a mild scale-dependent trend, with larger physical scales preferring lower S_8 values and a maximum low- versus high-k deviation of 1.80σ. Mock tests show that this behaviour 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.
ER  -