Abstract

X-ray observations of the hot gas filling the intra-cluster medium (ICM) provide a wealth of information on the dynamics of clusters of galaxies. The global equilibrium of the ICM is believed to be ensured by non-thermal and thermal pressure support sources, among which gas movements and the dissipation of energy through turbulent motions. Accurate mapping of turbulence using X-ray emission lines is challenging due to the lack of spatially resolved spectroscopy. Only future instruments such as the X-ray Integral Field Unit (X-IFU) on Athena will have the spatial and spectral resolution to quantitatively investigate the ICM turbulence over a broad range of spatial scales. Powerful diagnostics for these studies are line shift and the line broadening maps, and the second-order structure function. When estimating these quantities, instruments will be limited by uncertainties of their measurements, and by the sampling variance (also known as cosmic variance) of the observation. Here, we extend the formalism started in our companion Paper I to include the effect of statistical uncertainties of measurements in the estimation of these line diagnostics, in particular for structure functions. We demonstrate that statistics contribute to the total variance through different terms, which depend on the geometry of the detector, the spatial binning and the nature of the turbulent field. These terms are particularly important when probing the small scales of the turbulence. An application of these equations is performed for the X-IFU, using synthetic turbulent velocity maps of a Coma-like cluster. Results are in excellent agreement with the formulas both for the structure function estimation (≤3%) and its variance (≤10%). The expressions derived here and in Paper I are generic, and ensure an estimation of the total errors in any X-ray measurement of turbulent structure functions. They also open the way for optimisations in the upcoming instrumentation and in observational strategies.

Highlights

  • The X-ray emission of clusters of galaxies offers a phenomenal window to observe the thermodynamic and dynamic properties of the hot baryons composing the intra-cluster medium (ICM)

  • We addressed the challenge of computing these diagnostics and estimating their errors, related to both cosmic variance and measurement uncertainties

  • This work extends the approach started in our companion Paper I, which derives a formulation for the cosmic variance, and adds the contribution of finite statistics in the observations

Read more

Summary

Introduction

The X-ray emission of clusters of galaxies offers a phenomenal window to observe the thermodynamic and dynamic properties of the hot baryons composing the intra-cluster medium (ICM). Dynamics induced by constant 3D accretion from the medium surrounding the cluster and by merger events throughout their lifetime are strengthened by the role of central active galactic nuclei (AGNs), whose jets, outflows, and bubbles, drive powerful mechanical and radiative motions, stirring the ICM at every spatial scale (Fabian 2012; King & Pounds 2015; Gaspari & Sdowski 2017; Morganti 2017) Other effects present both at small (e.g. galaxy outflows) and large scales (e.g. sloshing, ram-stripping) create heterogeneities in the gas emission, thereby severely questioning the assumption of hydrostaticity. With the advent of high-resolution X-ray spectroscopy, powerful line diagnostics can be used to investigate turbulent motions These include the shift and broadening of a spectral line, and the computation of the structure function of the line-ofsight velocity field (Inogamov & Sunyaev 2003), related to the turbulent power spectrum of the ICM (Zhuravleva et al 2012).

Line centroid and broadening
The structure function
Estimators and value: the influence of finite statistics
Definitions and estimators of the structure functions
Expected value of the velocity shift and broadening
Expected value of the structure function
Variance of the structure function
Emission profile and turbulent power spectrum
Generation of the turbulent velocity field
Particle emission model
Post-processing of the data
General approach
Structure function estimation
Structure function variance estimation
Validation of error formulas and relative contribution
Practical estimation of errors
Towards optimising observation strategies
Conclusion
Neighbours and detector tessellation
Line shift
Line broadening
Expected average of the structure function
Expected variance of the structure function
Variance term
Covariance term
Findings
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call