The bispectrum covariance beyond Gaussianity: A log-normal approach

Abstract

To investigate and specify the statistical properties of cosmological fields with particular attention to possible non-Gaussian features, accurate formulae for the bispectrum and the bispectrum covariance are required. The bispectrum is the lowest-order statistic providing an estimate for non-Gaussianities of a distribution, and the bispectrum covariance depicts the errors of the bispectrum measurement and their correlation on different scales. Currently, there do exist fitting formulae for the bispectrum and an analytical expression for the bispectrum covariance, but the former is not very accurate and the latter contains several intricate terms and only one of them can be readily evaluated from the power spectrum of the studied field. Neglecting all higher-order terms results in the Gaussian approximation of the bispectrum covariance. We study the range of validity of this Gaussian approximation for two-dimensional non-Gaussian random fields. For this purpose, we simulate Gaussian and non-Gaussian random fields, the latter represented by log-normal fields and obtained directly from the former by a simple transformation. From the simulated fields, we calculate the power spectra, the bispectra, and the covariance from the sample variance of the bispectra, for different degrees of non-Gaussianity α, which is equivalent to the skewness on a given angular scale θg. We find that the Gaussian approximation provides a good approximation for α<0.6 and a reasonably accurate approximation for α< 1, both on scales >8θg. Using results from cosmic shear simulations, we estimate that the cosmic shear convergence fields are described by α<0.7 at θg~4". We therefore conclude that the Gaussian approximation for the bispectrum covariance is likely to be applicable in ongoing and future cosmic shear studies.

8 pages, 12 figures, submitted to Astronomy and Astrophysics

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• The squeezed matter bispectrum covariance with responses
• Constrained correlation functions from the Millennium Simulation
• Non-Gaussian likelihood of weak lensing power spectra