Mapping the Gas Turbulence in the Coma Cluster: Predictions for Astro-H

TL;DR

This paper predicts how Astro-H can map gas velocities and turbulence in the Coma cluster, estimating its ability to measure key turbulence parameters despite measurement errors and sampling limitations.

Contribution

It introduces a simulation framework to forecast Astro-H's capability to constrain turbulence properties in galaxy clusters, including the injection scale and Mach number.

Findings

Astro-H can measure the Mach number and injection scale of turbulence.

Measurement errors bias the velocity structure function upward, but can be corrected.

Astro-H's sensitivity to the dissipation scale and spectral slope is limited.

Abstract

Astro-H will be able for the first time to map gas velocities and detect turbulence in galaxy clusters. One of the best targets for turbulence studies is the Coma cluster, due to its proximity, absence of a cool core, and lack of a central active galactic nucleus. To determine what constraints Astro-H will be able to place on the Coma velocity field, we construct simulated maps of the projected gas velocity and compute the second-order structure function, an analog of the velocity power spectrum. We vary the injection scale, dissipation scale, slope, and normalization of the turbulent power spectrum, and apply measurement errors and finite sampling to the velocity field. We find that even with sparse coverage of the cluster, Astro-H will be able to measure the Mach number and the injection scale of the turbulent power spectrum--the quantities determining the energy flux down the turbulent cascade and the diffusion rate for everything that is advected by the gas (metals, cosmic rays, etc.). Astro-H will not be sensitive to the dissipation scale or the slope of the power spectrum in its inertial range, unless they are outside physically motivated intervals. We give the expected confidence intervals for the injection scale and the normalization of the power spectrum for a number of possible pointing configurations, combining the structure function and velocity dispersion data. Importantly, we also determine that measurement errors on the line shift will bias the velocity structure function upward, and show how to correct this bias.