A Random Walk Model for Dark Matter Halo Concentrations

For idealized (spherical, smooth) dark matter halos described by single-parameter density profiles (such as the Navarro–Frenk–White profile), there exists a one-to-one mapping between energy of halo and scale radius its profile. The therefore uniquely determines concentration parameter such halos. We exploit this fact to predict concentrations via random walk in space. Given full merger tree for halo, total internal each that is determined summing orbital energies progenitor show that, when calibrated, model can accurately reproduce mean concentration–mass relation measured N-body simulations reproduces more scatter than previous models. further test examining both autocorrelation radii across time correlations spin, comparing them results from cosmological simulations. In cases, we find our closely matches results. Our implemented within open-source Galacticus toolkit.