- Physical Review Journals
All JournalsPhysics Magazine
Access Provided by Xinjiang University

Physical Review D

Export citation

Export citation

Choose format for download:

Download Citation
  • Access by Xinjiang University

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
PDF

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.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (56)

  1. L. Van Waerbeke, Y. Mellier, T. Erben, J. C. Cuillandre, F. Bernardeau, R. Maoli, E. Bertin, H. J. McCracken, O. Le Fèvre, B. Fort, M. Dantel-Fort, B. Jain, and P. Schneider, Detection of correlated galaxy ellipticities from CFHT data: First evidence for gravitational lensing by large-scale structures, Astron. Astrophys. 358, 30 (2000), arXiv:astro-ph/0002500.
  2. N. Kaiser, G. Wilson, and G. A. Luppino, Large-scale cosmic shear measurements, arXiv:astro-ph/0003338.
  3. D. J. Bacon, A. R. Refregier, and R. S. Ellis, Detection of weak gravitational lensing by large-scale structure, Mon. Not. R. Astron. Soc. 318, 625 (2000).
  4. D. M. Wittman, J. A. Tyson, D. Kirkman, I. Dell’Antonio, and G. Bernstein, Detection of weak gravitational lensing distortions of distant galaxies by cosmic dark matter at large scales, Nature (London) 405, 143 (2000).
  5. R. D. Blandford, A. B. Saust, T. G. Brainerd, and J. V. Villumsen, The distortion of distant galaxy images by large-scale structure., Mon. Not. R. Astron. Soc. 251, 600 (1991).
  6. N. Kaiser, Weak gravitational lensing of distant galaxies, Astrophys. J. 388, 272 (1992).
  7. M. Bartelmann and P. Schneider, Weak gravitational lensing, Phys. Rep. 340, 291 (2001).
  8. M. Asgari et al., KiDS-1000 cosmology: Cosmic shear constraints and comparison between two point statistics, Astron. Astrophys. 645, A104 (2021).
  9. A. H. Wright et al., KiDS-Legacy: Cosmological constraints from cosmic shear with the complete kilo-degree survey, Astron. Astrophys. 703, A158 (2025).
  10. B. Stölzner et al., KiDS-Legacy: Consistency of cosmic shear measurements and joint cosmological constraints with external probes, Astron. Astrophys. 702, A169 (2025).
  11. A. Amon and G. Efstathiou, A non-linear solution to the S8 tension?, Mon. Not. R. Astron. Soc. 516, 5355 (2022).
  12. J. M. Bardeen, J. R. Bond, N. Kaiser, and A. S. Szalay, The statistics of peaks of Gaussian random fields, Astrophys. J. 304, 15 (1986).
  13. S. Gu, M.-A. Dor, L. van Waerbeke, M. Asgari, A. Mead, T. Tröster, and Z. Yan, On constraining cosmology and the halo mass function with weak gravitational lensing, Mon. Not. R. Astron. Soc. 525, 4871 (2023).
  14. C. Preston, A. Amon, and G. Efstathiou, A non-linear solution to the S8 tension—II. Analysis of DES Year 3 cosmic shear, Mon. Not. R. Astron. Soc. 525, 5554 (2023).
  15. M. White, R. Zhou, J. DeRose, S. Ferraro, S.-F. Chen, N. Kokron, S. Bailey, D. Brooks, J. García-Bellido, J. Guy, K. Honscheid, R. Kehoe, A. Kremin, M. Levi, N. Palanque-Delabrouille, C. Poppett, D. Schlegel, and G. Tarle, Cosmological constraints from the tomographic cross-correlation of DESI luminous red galaxies and Planck CMB lensing, J. Cosmol. Astropart. Phys. 02 (2022) 007.
  16. M.-X. Lin, B. Jain, M. Raveri, E. J. Baxter, C. Chang, M. Gatti, S. Lee, and J. Muir, Late time modification of structure growth and the S8 tension, Phys. Rev. D 109, 063523 (2024).
  17. K. Naidoo, M. Jaber, W. A. Hellwing, and M. Bilicki, Dark matter solution to the H0 and S8 tensions, and the integrated Sachs-Wolfe void anomaly, Phys. Rev. D 109, 083511 (2024).
  18. S. Gu, L. van Waerbeke, F. Bernardeau, and R. Dalal, Mitigating nonlinear systematics in weak lensing surveys: The Bernardeau-Nishimichi-Taruya approach, Phys. Rev. D 111, 083530 (2025).
  19. M. P. van Daalen, I. G. McCarthy, and J. Schaye, Exploring the effects of galaxy formation on matter clustering through a library of simulation power spectra, Mon. Not. R. Astron. Soc. 491, 2424 (2020).
  20. F. Bernardeau, T. Nishimichi, and A. Taruya, Cosmic shear full nulling: Sorting out dynamics, geometry and systematics, Mon. Not. R. Astron. Soc. 445, 1526 (2014).
  21. S. Gu, L. van Waerbeke, F. Bernardeau, and S. Fabbro, Mitigating nonlinear systematics in weak lensing surveys. II. Stability and diagnostics with intrinsic alignment, Phys. Rev. D 113, 023528 (2026).
  22. P. L. Taylor, F. Bernardeau, and E. Huff, x -cut cosmic shear: Optimally removing sensitivity to baryonic and nonlinear physics with an application to the dark energy survey year 1 shear data, Phys. Rev. D 103, 043531 (2021).
  23. D. Alonso, J. C. Hill, R. Hložek, and D. N. Spergel, Measurement of the thermal sunyaev-zel’dovich effect around cosmic voids, Phys. Rev. D 97, 063514 (2018).
  24. C. García-García, D. Alonso, and E. Bellini, Disconnected pseudo-Cℓ covariances for projected large-scale structure data, J. Cosmol. Astropart. Phys. 11 (2019) 043.
  25. D. N. Limber, The analysis of counts of the extragalactic nebulae in terms of a fluctuating density field. II., Astrophys. J. 119, 655 (1954).
  26. S. Bridle and L. King, Dark energy constraints from cosmic shear power spectra: Impact of intrinsic alignments on photometric redshift requirements, New J. Phys. 9, 444 (2007).
  27. M. A. Troxel et al. (DES Collaboration), Dark energy survey year 1 results: Cosmological constraints from cosmic shear, Phys. Rev. D 98, 043528 (2018).
  28. D. Alonso, J. Sanchez, and A. Slosar, A unified pseudo-Cℓ framework, Mon. Not. R. Astron. Soc. 484, 4127 (2019).
  29. A. H. Wright et al., The fifth data release of the kilo degree survey: Multi-epoch optical/NIR imaging covering wide and legacy-calibration fields, Astron. Astrophys. 686, A170 (2024).
  30. J. T. A. de Jong, G. A. Verdoes Kleijn, K. H. Kuijken, and E. A. Valentijn, The kilo-degree survey, Exp. Astron. 35, 25 (2013).
  31. D. Enard, The ESO very large telescope project—present status, in Advanced Technology Optical Telescopes III, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 628, edited by L. D. Barr (SPIE, Bellingham, 1986), pp. 221–226, 10.1117/12.963533.
  32. M. Capaccioli, D. Mancini, and G. Sedmak, The VLT survey telescope: A status report, The Messenger 120, 10 (2005).
  33. J. P. Emerson and W. J. Sutherland, VISTA: Status and performance, in Ground-Based and Airborne Telescopes III, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 7733, edited by L. M. Stepp, R. Gilmozzi, and H. J. Hall (SPIE, Bellingham, 2010), p. 773306, 10.1117/12.857105.
  34. W. Sutherland et al., The Visible and Infrared Survey Telescope for Astronomy (VISTA): Design, technical overview, and performance, Astron. Astrophys. 575, A25 (2015).
  35. K. Kuijken et al., The fourth data release of the kilo-degree survey: Ugri imaging and nine-band optical-IR photometry over 1000 square degrees, Astron. Astrophys. 625, A2 (2019).
  36. B. Giblin et al., KiDS-1000 catalogue: Weak gravitational lensing shear measurements, Astron. Astrophys. 645, A105 (2021).
  37. K. Kuijken, OmegaCAM: ESO’s newest imager, The Messenger 146, 8 (2011).
  38. A. Edge, W. Sutherland, K. Kuijken, S. Driver, R. McMahon, S. Eales, and J. P. Emerson, The VISTA Kilo-degree Infrared Galaxy (VIKING) Survey: Bridging the gap between low and high redshift, The Messenger 154, 32 (2013).
  39. D. G. York et al. (SDSS Collaboration), The Sloan Digital Sky Survey: Technical summary, Astron. J. 120, 1579 (2000).
  40. C. Blake, S. Brough, W. Couch, K. Glazebrook, G. Poole, T. Davis, M. Drinkwater, R. Jurek, K. Pimbblet, M. Colless, R. Sharp, S. Croom, M. Pracy, D. Woods, B. Madore, C. Martin, and T. Wyder, The WiggleZ dark energy survey, Astron. Geophys. 49, 5.19 (2008).
  41. C. Blake, A. Amon, M. Childress, T. Erben, K. Glazebrook, J. Harnois-Deraps, C. Heymans, H. Hildebrandt, S. R. Hinton, S. Janssens, A. Johnson, S. Joudaki, D. Klaes, K. Kuijken, C. Lidman, F. A. Marin, D. Parkinson, G. B. Poole, and C. Wolf, The 2-degree field lensing survey: Design and clustering measurements, Mon. Not. R. Astron. Soc. 462, 4240 (2016).
  42. B. Abareshi et al. (DESI Collaboration), Overview of the instrumentation for the dark energy spectroscopic instrument, Astron. J. 164, 207 (2022).
  43. S. P. Driver et al., Galaxy And Mass Assembly (GAMA): Data release 4 and the z<0.1 total and z<0.08 morphological galaxy stellar mass functions, Mon. Not. R. Astron. Soc. 513, 439 (2022).
  44. N. Benítez, Bayesian photometric redshift estimation, Astrophys. J. 536, 571 (2000).
  45. T. Kohonen, Self-Organizing Maps (Springer-Verlag, Berlin, Heidelberg, 2001), Vol. 30, p. 501, 10.1007/978-3-642-56927-2.
  46. J. E. Geach, Unsupervised self-organized mapping: A versatile empirical tool for object selection, classification and redshift estimation in large surveys, Mon. Not. R. Astron. Soc. 419, 2633 (2012).
  47. A. H. Wright, H. Hildebrandt, J. L. van den Busch, C. Heymans, B. Joachimi, A. Kannawadi, and K. Kuijken, KiDS+VIKING-450: Improved cosmological parameter constraints from redshift calibration with self-organising maps, Astron. Astrophys. 640, L14 (2020).
  48. K. M. Gorski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, healpix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere, Astrophys. J. 622, 759 (2005).
  49. A. Nicola, C. García-García, D. Alonso, J. Dunkley, P. G. Ferreira, A. Slosar, and D. N. Spergel, Cosmic shear power spectra in practice, J. Cosmol. Astropart. Phys. 03 (2021) 067.
  50. N. E. Chisari et al. (LSST Dark Energy Science Collaboration), Core cosmology library: Precision cosmological predictions for LSST, Astrophys. J. Suppl. Ser. 242, 2 (2019).
  51. R. Reischke et al., KiDS-Legacy: Covariance validation and the unified onecovariance framework for projected large-scale structure observables, Astron. Astrophys. 699, A124 (2025).
  52. R. Mandelbaum et al. (The LSST Dark Energy Science Collaboration), The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document, 10.2172/1471560 (2018).
  53. J. U. Lange, nautilus: Boosting Bayesian importance nested sampling with deep learning, Mon. Not. R. Astron. Soc. 525, 3181 (2023).
  54. N. Aghanim et al. (Planck Collaboration), Planck 2018 results. VI. Cosmological parameters, Astron. Astrophys. 641, A6 (2020).
  55. https://github.com/ShimingGu/BNT_PRD/.
  56. A. Lewis, getdist: A python package for analysing Monte Carlo samples, J. Cosmol. Astropart. Phys. 08 (2025) 025.

Outline

Information

Phys. Rev. D 114, 083535– Published 23 October, 2026
  • Received 13 July 2026
  • Accepted 9 September 2026
Article part of CHORUS
Crossmark - Check for updates button

© 2026 American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation