Non-Gaussian error bars in galaxy surveys-I

We propose a method to estimate non-Gaussian error bars on the matter power spectrum from galaxy surveys in the presence of non-trivial survey selection functions. The estimators are often obtained from formalisms like Feldmann, Kaiser and Peacock (FKP) and pseudo-KarhunenLoeve (PKL), which rely on the assumption that the underlying field is Gaussian. The Monte Carlo method is more accurate but involves the tedious process of running and cross-correlating a large number of N-body simulations, in which the survey volume is embedded. From 200 N-body simulations, we extract a non-linear covariance matrix as a function of two scales and of the angle between two Fourier modes. All the non-Gaussian features of that matrix are then simply parametrized in terms of a few fitting functions and eigenvectors. We furthermore develop a fast and accurate strategy that combines our parametrization with a general galaxy survey selection function, and incorporate non-Gaussian Poisson uncertainty. We describe how to incorporate these two distinct non-Gaussian contributions into a typical analysis pipeline, and apply our method with the selection function from the 2dFGRS. We find that the observed Fourier modes correlate at much larger scales than that predicted by both FKP formalism or pure N-body simulations in a top hat selection function. In particular, the observed Fourier modes are already 50 per cent correlated at k similar to 0.1 h Mpc-1, and the non-Gaussian fractional variance on the power spectrum is about a factor of 3.0 larger than the FKP prescription. At k similar to 0.4 h Mpc-1, the deviations are an order of magnitude.