A Redshift Survey of Nearby Galaxy Groups: the Shape of the Mass Density Profile

We constrain the mass profile and orbital structure of nearby groups and clusters of galaxies. Our method yields the joint probability distribution of the density slope n, the velocity anisotropy beta, and the turnover radius r0 for these systems. The measurement technique does not use results from N-body simulations as priors. We incorporate 2419 new redshifts in the fields of 41 systems of galaxies with z < 0.04. The new groups have median velocity dispersion sigma=360 km/s. We also use 851 archived redshifts in the fields of 8 nearly relaxed clusters with z < 0.1. Within R < 2 r200, the data are consistent with a single power law matter density distribution with slope n = 1.8-2.2 for systems with sigma < 470 km/s, and n = 1.6-2.0 for those with sigma > 470 km/s (95% confidence). We show that a simple, scale-free phase space distribution function f(E,L^2) ~ (-E)^(alpha-1/2) L^(-2 \beta) is consistent with the data as long as the matter density has a cusp. Using this DF, matter density profiles with constant density cores (n=0) are ruled out with better than 99.7% confidence.Comment: 22 pages; accepted for publication in the Astrophysical Journa

A spatial analysis of multivariate output from regional climate models

Climate models have become an important tool in the study of climate and climate change, and ensemble experiments consisting of multiple climate-model runs are used in studying and quantifying the uncertainty in climate-model output. However, there are often only a limited number of model runs available for a particular experiment, and one of the statistical challenges is to characterize the distribution of the model output. To that end, we have developed a multivariate hierarchical approach, at the heart of which is a new representation of a multivariate Markov random field. This approach allows for flexible modeling of the multivariate spatial dependencies, including the cross-dependencies between variables. We demonstrate this statistical model on an ensemble arising from a regional-climate-model experiment over the western United States, and we focus on the projected change in seasonal temperature and precipitation over the next 50 years.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS369 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org