Resolving observable features of galaxy cluster merger 'El Gordo' using idealized cluster models in SPH simulations

TL;DR

This study uses idealized SPH simulations with NFW and β-model profiles to replicate observable features of the galaxy cluster 'El Gordo', constraining merger parameters to match X-ray and SZ observations.

Contribution

It introduces a detailed parameter study of idealized cluster mergers to reproduce specific observational features of 'El Gordo', supporting an off-axis, high-speed collision scenario.

Findings

Reproduced wake-like X-ray feature behind the secondary subcluster.

Identified merger parameters P ~ 800 kpc and ε = 0.6 that match observed cluster shape.

Supported an off-axis, high-speed collision as the merger scenario.

Abstract

The merger scenario of two galaxy subclusters to form the massive galaxy cluster 'El Gordo' is investigated using smoothed particle hydrodynamics (SPH) simulations. Idealized cluster models are used to initialize the states of both subclusters prior to the merger, assuming the commonly used Navarro-Frenk-White (NFW) dark matter (DM) density profile and solving the hydrostatic equilibrium equation for a ${\beta}$-model with ${\beta}$ = 0.6 to obtain the gas density profile. The impact parameter (P) and zero-energy orbit fraction (${\epsilon}$) of the two merging subclusters are varied to put constraints on the space of parameter values which result in projections of X-ray luminosity, Sunyaev-Zel'dovich (SZ) effect, and DM density that correlate well with observational features of 'El Gordo'. We are able to reproduce the remarkable wake-like feature seen in X-ray observations that trails after the secondary (bullet) subcluster as well as the rough shape of the combined cluster with P ~ 800 kpc and ${\epsilon}$ = 0.6, which corroborate the results of prior simulations that support an off-axis, high-speed collision as the merger scenario. We argue that the large separation distance between the mass centers of the two subclusters in this merger scenario may be resolved by finding a better fit for other parameters in the idealized subcluster models.