Effective halo model: Creating a physical and accurate model of the matter power spectrum and cluster counts

We introduce a physically motivated model of the matter power spectrum, based on the halo model and perturbation theory. This model achieves 1% accuracy on all k-scales between k = 0.02h Mpc(-1) to k = 1h Mpc(-1). Our key ansatz is that the number density of halos depends on the nonlinear density contrast filtered on some unknown scale R. Using the effective field theory of large scale structure to evaluate the two-halo term, we obtain a model for the power spectrum with only two fitting parameters: R and the effective sound speed, which encapsulates small-scale physics. This is tested with two suites of cosmological simulations across a broad range of cosmologies and found to be highly accurate. Due to its physical motivation, the statistics can be easily extended beyond the power spectrum; we additionally derive the one-loop covariance matrices of cluster counts and their combination with the matter power spectrum. This yields a significantly better fit to simulations than previous models, and includes a new model for supersample effects, which is rigorously tested with separate universe simulations. At low redshift, we find a significant (similar to 10%) exclusion covariance from accounting for the finite size of halos which has not previously been modeled. Such power spectrum and covariance models will enable joint analysis of upcoming large-scale structure surveys, gravitational lensing surveys, and cosmic microwave background maps on scales down to the nonlinear scale. We provide a publicly released Python code.