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X+yX+y: insights on gas thermodynamics from the combination of X-ray and thermal Sunyaev-Zel'dovich data cross-correlated with cosmic shear

Dec 16, 2024
20 pages
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Abstract: (arXiv)
We measure the cross-correlation between cosmic shear from the third-year release of the Dark Energy Survey, thermal Sunyaev-Zel'dovich (tSZ) maps from Planck, and X-ray maps from ROSAT. We investigate the possibility of developing a physical model able to jointly describe both measurements, simultaneously constraining the spatial distribution and thermodynamic properties of hot gas. We find that a relatively simple model is able to describe both sets of measurements and to make reasonably accurate predictions for other observables (the tSZ auto-correlation, its cross-correlation with X-rays, and tomographic measurements of the bias-weighted mean gas pressure). We show, however, that contamination from X-ray AGN, as well as the impact of non-thermal pressure support, must be incorporated in order to fully resolve tensions in parameter space between different data combinations. We obtain simultaneous constraints on the mass scale at which half of the gas content has been expelled from the halo, log10(Mc)=14.83−0.23+0.16\mathrm{log}_{10}(M_c)=14.83^{+0.16}_{-0.23}, on the polytropic index of the gas, Γ=1.144−0.013+0.016\Gamma=1.144^{+0.016}_{-0.013}, and on the ratio of the central gas temperature to the virial temperature αT=1.30−0.28+0.15\alpha_T=1.30^{+0.15}_{-0.28}.
Note:
  • 20 pages, 10 figures
  • 1.5
    • 2.0 2.5 α T 14.0 14.4 14.8 log10(Mc) 1.15 1.20 1.25
      • CγX
      • [3]
        2D marginalised posterior distributions for our minimal hydrodynamic model, derived from the X-ray-shear correlation (blue), from the tSZ-shear correlation (orange), and from their combination (green). Angular power spectrum predictions from best-fit models of these three data combinations are shown in Fig. 2. Note that the constraint on αT from the shear-tSZ correlation is fully driven by the prior bounds on log10(Mc). are able to describe the joint dataset reasonably well. The best-fit chi-squared statistic for the full data vector is χ2 = 204.0/189, corresponding to a PTE of 22%. The corresponding χ2 values for the individual power spectra, and their PTEs, are listed in
        • Table I. We
        • [4]
          2D marginalised posterior distributions
          • for CγX