Dark matter-dominated cluster-scale halos act as an important cosmological probe and provide a key testing ground for structure formation theory. Focusing on their mass profiles, we have carried out (gravity-only) simulations of the concordance LCDM
cosmology, covering a mass range of 2.10^{12}-2.10^{15} solar mass/h and a redshift range of z=0-2, while satisfying the associated requirements of resolution and statistical control. When fitting to the Navarro-Frenk-White profile, our concentration-mass (c-M) relation differs in normalization and shape in comparison to previous studies that have limited statistics in the upper end of the mass range. We show that the flattening of the c-M relation with redshift is naturally expressed if c is viewed as a function of the peak height parameter, u. Unlike the c-M relation, the slope of the c- u relation is effectively constant over the redshift range z=0-2, while the amplitude varies by ~30% for massive clusters. This relation is, however, not universal: Using a simulation suite covering the allowed wCDM parameter space, we show that the c- u relation varies by about +/- 20% as cosmological parameters are varied. At fixed mass, the c(M) distribution is well-fit by a Gaussian with sigma_c/c = 0.33, independent of the radius at which the concentration is defined, the halo dynamical state, and the underlying cosmology. We compare the LCDM predictions with observations of halo concentrations from strong lensing, weak lensing, galaxy kinematics, and X-ray data, finding good agreement for massive clusters (M > 4.10^{14} solar mass/h), but with some disagreements at lower masses. Because of uncertainty in observational systematics and modeling of baryonic physics, the significance of these discrepancies remains unclear.
We describe an approximate statistical model for the sample variance distribution of the non-linear matter power spectrum that can be calibrated from limited numbers of simulations. Our model retains the common assumption of a multivariate Normal dis
tribution 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.
