The Impact of Non-Gaussianity upon Cosmological Forecasts

Abstract

The primary science driver for 3D galaxy surveys is their potential to constrain cosmological parameters. Forecasts of these surveys' effectiveness typically assume Gaussian statistics for the underlying matter density, despite the fact that the actual distribution is decidedly non-Gaussian. To quantify the effect of this assumption, we employ an analytic expression for the power spectrum covariance matrix to calculate the Fisher information for BAO-type model surveys. We find that for typical number densities, at $k_{\max }=0.5h$ Mpc${}^{-1}$, Gaussian assumptions significantly overestimate the information on all parameters considered, in some cases by up to an order of magnitude. However, after marginalizing over a six-parameter set, the form of the covariance matrix (dictated by $N$-body simulations) causes the majority of the effect to shift to the "amplitude-like" parameters, leaving the others virtually unaffected. We find that Gaussian assumptions at such wavenumbers can underestimate the dark energy parameter errors by well over 50 per cent, producing dark energy figures of merit almost 3 times too large. Thus, for 3D galaxy surveys probing the non-linear regime, proper consideration of non-Gaussian effects is essential.

9 pages, 5 figures, submitted to MNRAS

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• Precision Prediction for the Cosmological Density Distribution
• Density-dependent clustering: I. Pulling back the curtains on motions of the BAO peak
• Inadequacy of internal covariance estimation for super-sample covariance
• Constraints on Dark Matter-Neutrino Interaction from 21-cm Cosmology and Forecasts on SKA1-Low
• The gravitational and lensing-ISW bispectrum of 21cm radiation
• An Ising model for galaxy bias
• Cosmic variance mitigation in measurements of the integrated Sachs-Wolfe effect
• Predicting the Sufficient-Statistics Power Spectrum for Galaxy Surveys: A Recipe for $P_{A\ast }(k)$
• Statistical Nuances in BAO Analysis: Likelihood Formulations and Non-Gaussianities
• Indicator Power Spectra: Surgical Excision of Non-linearities and Covariance Matrices for Counts in Cells