KIPAC Tea Talk: Will Dawson (UC Davis)

Dark Matter is one of the great outstanding mysteries.  Although still in its infancy, the study of merging galaxy clusters has been shown to be one of the best means of determining the nature of dark matter.  However, to properly constrain the dark matter cross-section it is necessary to understand the dynamics of the merger, as the inferred cross-section is both a function of the velocity of the collision and the observed time since collision.  While N-body simulations provide the best understanding of the merging system dynamics they are computationally expensive and often incapable of determining the probability distribution functions (PDF’s) of the merger parameters.  Simple analytic methods (e.g. the “timing argument”) are often applied to dissociative mergers but the assumptions of these methods cause them to fail near the collision time and these methods also fail to determine the PDF’s of the merger parameters.  I will introduce an intermediate Monet Carlo method that discerns the merger dynamics of systems and will propagate the errors of the measured cluster parameters in an accurate and Bayesian manor. This method performs superior to existing analytic models and is in better than 10% agreement with N-body simulations.  Applying this method to the Bullet and Musket Ball Clusters I will show that the Musket Ball Cluster can provide a factor of 5 improvement in dark matter self-interaction cross-section constraint of the Bullet Cluster, with the possibility of measuring the cross-section if it is >0.2 cm^2/g.