2017/09/30 by Kwan Chuen Chan, Azadeh Moradinezhad Dizgah, Jorge Noreña · 3 citations
Computer Science · Mathematics · Physics and Astronomy · #Bispectrum #Blind Source Separation Techniques #Computer science #Cosmology and Gravitation Theories #Covariance #Galaxies: Formation, Evolution, Phenomena #Mathematics #Spectral density #Statistics #astro-ph.CO
paper · pdf · doi:10.1103/physrevd.97.043532
published as Phys. Rev. D 97, 043532 (2018) · 24 pages, 12 figures, previous Eq 50 corrected, an appendix on the tidal effects added, match to the published version
arxiv created 2018/02/26 · openalex publication_date 2018/02/28 · arxiv updated 2018/03/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Modes with wavelengths larger than the survey window can have significant impact on the covariance within the survey window. The supersample covariance has been recognized as an important source of covariance for the power spectrum on small scales, and it can potentially be important for the bispectrum covariance as well. In this paper, using the response function formalism, we model the supersample covariance contributions to the bispectrum covariance and the cross-covariance between the power spectrum and the bispectrum. The supersample covariances due to the long-wavelength density and tidal perturbations are investigated, and the tidal contribution is a few orders of magnitude smaller than the density one because in configuration space the bispectrum estimator involves angular averaging and the tidal response function is anisotropic. The impact of the super-survey modes is quantified using numerical measurements with periodic box and sub-box setups. For the matter bispectrum, the ratio between the supersample covariance correction and the small-scale covariance---which can be computed using a periodic box---is roughly an order of magnitude smaller than that for the matter power spectrum. This is because for the bispectrum, the small-scale non-Gaussian covariance is significantly larger than that for the power spectrum. For the cross-covariance, the supersample covariance is as important as for the power spectrum covariance. The supersample covariance prediction with the halo model response function is in good agreement with numerical results.