On the interpretation of XRISM X-ray measurements of turbulence in the intracluster medium: a comparison with cosmological simulations

TL;DR

This study tackles whether XRISM's turbulence measurements in the Coma cluster are compatible with cosmological simulations. Using a high-resolution Coma-like cluster simulation and a fixed-scale turbulence filter, the authors show that a patchy, multi-region turbulence field yields velocity structure functions and X-ray line broadening compatible with XRISM data, without requiring an unnaturally steep spectrum. The results demonstrate that a Kolmogorov-like spectrum can persist in a stratified, intermittently stirred ICM and that emissivity weighting and turbulence intermittency reduce observable line widths relative to homogeneous models, leading to a modest inferred non-thermal pressure (about $5\\%$ within $R\sim 1.5$ Mpc for $\Lambda_t=300$ kpc) while the true turbulent content remains higher along certain sightlines. Overall, the work highlights the crucial role of realistic turbulence modeling and advanced simulations in interpreting XRISM measurements and constraining ICM dynamics and non-thermal pressure support.

Abstract

We investigate whether the properties of turbulent gas motions recently measured via X-ray spectroscopy in the Coma cluster of galaxies by XRISM are in tension with the turbulent picture established by current numerical cosmological simulations. We use a high-resolution simulation of a Coma-like cluster and show that the simulation yields velocity structure functions and X-ray line-widths that are compatible with those measured by the XRISM observations of Coma. In particular, it has been previously suggested that a much steeper turbulence spectrum than the Kolmogorov would be needed to explain the XRISM observations under a homogeneous, cluster volume-filling turbulence model. Our results show that this tension is overcome thanks to the more complicated turbulent picture in cosmological simulations, that indeed shows a patchy distribution of turbulent regions in galaxy clusters, with a spectrum that is generally consistent with a Kolmogorov power-law over a fairly wide range of scales. More generally, our study highlights the fact that the interpretation of XRISM data of galaxy clusters depends on the turbulence model used and the importance of combining data and advanced simulations in the future steps.

Figures (9)

• Figure 1: Projected X-ray surface brightness in the [5-7] keV energy range for our simulated cluster (top left), average X-ray weighted gas temperature (lower left), average gas velocity along the line of sight, either using a volume-weighting procedure (top centre) or an X-ray emissivity weighting along the line of sight, within pixel of $90 \times 90 \rm ~kpc^2$ to mimic XRISM field of view for Coma (bottom centre). Gas velocity dispersion along the line of sight, again either using a volume-weighting procedure (top right) or an X-ray emissivity weighting along the line of sight, for $90 \times 90 \rm ~kpc^2$ pixels (bottom right). The black squares refer to the regions used to produce the line emission profiles given in Fig.\ref{['fig:line']}.
• Figure 2: Thin lines with different colours: simulated VSFs for 50 random lines of sight, with area $90 \times 90 \rm ~kpc^2$, randomly extracted from our 2D velocity map and with separations as in the XRISM observation of Coma. Blue lines: full 3-dimensional VSF for the velocity along the line of sight component, using $10^7$ cells in the cluster volume. The grey points are the XRISM measurements for Coma, the additional dashed lines show the best-fit models derived Coma_XRISM for a Kolmogorov spectrum (red) or a much steeper one (green), while the grey shaded area show the 68% cosmic variance uncertainty and the measurement statistical errors in the same work.
• Figure 3: Simulated line profiles for the four FOVs indicated in Fig.\ref{['fig:map0']} (color lines) compared with the reconstructed line models for the XRISM observation of the Coma cluster produced by Coma_XRISM, given by the two shaded areas.
• Figure 4: Top panel: histograms of X-ray weighted velocity dispersion along the LOS and of volume weighted filtered turbulent velocity dispersion (after the application of our small-scale filtering with a $\Lambda=300 \rm ~kpc$ scale) for $90 \times 90 \rm ~kpc^2$ pixels maps. The additional vertical lines give the values inferred for the central FOVs observed by XRISM in the central region of Coma. Central panel: histograms for the ratio between the two estimates of the velocity dispersion above. Bottom panel: ratio between the X-ray weighted velocity dispersion the filtered turbulent velocity dispersion within the $\Lambda=300 \rm ~kpc$ scale, as a function of the X-ray emission of cells.
• Figure 5: 3-dimensional radial profile of the non-thermal to total pressure ratio within the radius, considering the total (unfiltered) gas velocity field ($p_{kin}$) or four different choices for the small-scale filtering length ($\Lambda=150, 300, 600$ and $900$$\rm kpc$, where $p_{turb,300}$ is our reference estimate as discussed in the main text). The horizontal grey strip gives the estimate for the Coma cluster by Coma_XRISM.