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Perturbation theory approach to predict the covariance matrices of the galaxy power spectrum and bispectrum in redshift space

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arxiv 1908.06234 v2 pith:PI4ENKII submitted 2019-08-17 astro-ph.CO

classification astro-ph.CO
keywords powerbispectrumcovariancematricesperturbationpredictredshiftspace
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In this paper, we predict the covariance matrices of both the power spectrum and the bispectrum, including full non-Gaussian contributions, redshift space distortions, linear bias effects and shot-noise corrections, using perturbation theory (PT). To quantify the redshift-space distortion effect, we focus mainly on the monopole and quadrupole components of both the power and bispectra. We, for the first time, compute the 5- and 6-point spectra to predict the cross-covariance between the power and bispectra, and the auto-covariance of the bispectrum in redshift space. We test the validity of our calculations by comparing them with the covariance matrices measured from the MultiDark-Patchy mock catalogues that are designed to reproduce the galaxy clustering measured from the Baryon Oscillation Spectroscopic Survey Data Release 12. We argue that the simple, leading-order perturbation theory works because the shot-noise corrections for the Patchy mocks are more dominant than other higher-order terms we ignore. In the meantime, we confirm some discrepancies in the comparison, especially of the cross-covariance. We discuss potential sources of such discrepancies. We also show that our PT model reproduces well the cumulative signal-to-noise of the power spectrum and the bispectrum as a function of maximum wavenumber, implying that our PT model captures successfully essential contributions to the covariance matrices.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Gaussian Covariance Matrix for Joint Pre- and Post-Reconstruction Full-Shape Power Spectrum Analysis

    astro-ph.CO 2026-08 conditional novelty 6.0 of 10

    A semi-analytical Gaussian covariance for joint pre-, post-, and cross-reconstruction power spectra is validated against mocks and reproduces parameter constraints from mock-based covariance on conservative scales.

  2. Perturbative Likelihoods for Large-Scale Structure of the Universe

    astro-ph.CO 2025-05 conditional novelty 6.0 of 10

    A perturbative derivation shows that the large-scale structure likelihood is automatically expressed in terms of the tree-level power spectrum, tree-level bispectrum, and the (2,2) one-loop power spectrum correction.

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