Simulations and cosmological inference: A statistical model for power spectra means and covariances

Abstract

We describe an approximate statistical model for the sample variance distribution of the nonlinear matter power spectrum that can be calibrated from limited numbers of simulations. Our model retains the common assumption of a multivariate normal distribution for the power spectrum band powers but takes full account of the (parameter-dependent) power spectrum covariance. The model is calibrated using an extension of the framework in Habib et al. (2007) to train Gaussian processes for the power spectrum mean and covariance given a set of simulation runs over a hypercube in parameter space. We demonstrate the performance of this machinery by estimating the parameters of a power-law model for the power spectrum. Within this framework, our calibrated sample variance distribution is robust to errors in the estimated covariance and shows rapid convergence of the posterior parameter constraints with the number of training simulations.