Probing Gas Motions in the Intra-Cluster Medium: A Mixture Model Approach

TL;DR

This paper introduces a novel spectral analysis method using Gaussian mixture models to analyze gas motions in galaxy clusters, leveraging high-resolution spectra to distinguish bulk flows from turbulence.

Contribution

The paper develops a new spectral analysis technique that utilizes the entire line shape with mixture models, enabling detailed inference of gas velocity components in the intra-cluster medium.

Findings

Mixture parameters can be constrained at ~10% accuracy when components differ in width.

Parameters can be constrained at ~1% accuracy when components differ in mean.

The method effectively distinguishes bulk motions from turbulence in simulated cluster spectra.

Abstract

Upcoming high spectral resolution telescopes, particularly Astro-H, are expected to finally deliver firm quantitative constraints on turbulence in the intra-cluster medium (ICM). We develop a new spectral analysis technique which exploits not just the line width but the entire line shape, and show how the excellent spectral resolution of Astro-H can overcome its relatively poor spatial resolution in making detailed infer- ences about the velocity field. The spectrum is decomposed into distinct components, which can be quantitatively analyzed using Gaussian mixture models. For instance, bulk flows and sloshing produce components with offset means, while partial volume- filling turbulence from AGN or galaxy stirring leads to components with different widths. The offset between components allows us to measure gas bulk motions and separate them from small-scale turbulence, while component fractions and widths con- strain the emission weighted volume and turbulent energy density in each component. We apply mixture modeling to a series of analytic toy models as well as numerical simu- lations of clusters with cold fronts and AGN feedback respectively. From Markov Chain Monte Carlo and Fisher matrix estimates which include line blending and continuum contamination, we show that the mixture parameters can be accurately constrained with Astro-H spectra: at a \sim 10% level when components differ significantly in width, and a \sim 1% level when they differ significantly in mean value. We also study error scalings and use information criteria to determine when a mixture model is preferred. Mixture modeling of spectra is a powerful technique which is potentially applicable to other astrophysical scenarios.