The Dynamics of Merging Clusters: A Monte Carlo Solution Applied to the Bullet and Musket Ball Clusters

TL;DR

This paper introduces a Bayesian Monte Carlo method to accurately model the dynamics of merging galaxy clusters, improving upon existing analytic models and aiding dark matter research.

Contribution

A novel Monte Carlo approach for analyzing merging galaxy clusters that propagates uncertainties and provides accurate dynamic parameters with minimal prior information.

Findings

Method achieves 10% accuracy in dynamic parameter estimation.

Verifies results against hydrodynamic N-body simulations.

Applicable to large samples of merging clusters with fast computation.

Abstract

Merging galaxy clusters have become one of the most important probes of dark matter, providing evidence for dark matter over modified gravity and even constraints on the dark matter self-interaction cross-section. To properly constrain the dark matter cross-section it is necessary to understand the dynamics of the merger, as the inferred cross-section is a function of both the velocity of the collision and the observed time since collision. While the best understanding of merging system dynamics comes from N-body simulations, these are computationally intensive and often explore only a limited volume of the merger phase space allowed by observed parameter uncertainty. Simple analytic models exist but the assumptions of these methods invalidate their results near the collision time, plus error propagation of the highly correlated merger parameters is unfeasible. To address these weaknesses I develop a Monte Carlo method to discern the properties of dissociative mergers and propagate the uncertainty of the measured cluster parameters in an accurate and Bayesian manner. I introduce this method, verify it against an existing hydrodynamic N-body simulation, and apply it to two known dissociative mergers: 1ES 0657-558 (Bullet Cluster) and DLSCL J0916.2+2951 (Musket Ball Cluster). I find that this method surpasses existing analytic models - providing accurate (10% level) dynamic parameter and uncertainty estimates throughout the merger history. This coupled with minimal required a priori information (subcluster mass, redshift, and projected separation) and relatively fast computation (~6 CPU hours) makes this method ideal for large samples of dissociative merging clusters.