The Open Journal of Astrophysics (Nov 2023)

Dissecting the Thermal SZ Power Spectrum by Halo Mass and Redshift in SPT-SZ Data and Simulations

  • Josemanuel Hernandez,
  • Lindsey Bleem,
  • Thomas Crawford,
  • Nicholas Huang,
  • Yuuki Omori,
  • Srinivasan Raghunathan,
  • Christian Reichardt

Journal volume & issue
Vol. 6

Abstract

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We explore the relationship between the thermal Sunyaev-Zel'dovich (tSZ) power spectrum amplitude and the halo mass and redshift of galaxy clusters in South Pole Telescope (SPT) data, in comparison with three $N$-body simulations combined with semi-analytical gas models of the intra-cluster medium. Specifically, we calculate both the raw and fractional power contribution to the full tSZ power spectrum amplitude at $\ell = 3000$ from clusters as a function of halo mass and redshift. We use nine mass bins in the range $1 \times 10^{14}\ M_\odot\ h^{-1} < M_{500} < 2 \times 10^{15}\ M_\odot\ h^{-1}$, and two redshift bins defined by $0.25 < z < 0.59$ and $0.59 < z < 1.5$. We additionally divide the raw power contribution in each mass bin by the number of clusters in that bin, as a metric for comparison of different gas models. At lower masses, the SPT data prefers a model that includes a mass-dependent bound gas fraction component and relatively high levels of AGN feedback, whereas at higher masses there is a preference for a model with a lower amount of feedback and a complete lack of non-thermal pressure support. The former provides the best fit to the data overall, in regards to all metrics for comparison. Still, discrepancies exist and the data notably exhibits a steep mass-dependence which all of the simulations fail to reproduce. This suggests the need for additional mass- and redshift-dependent adjustments to the gas models of each simulation, or the potential presence of contamination in the data at halo masses below the detection threshold of SPT-SZ. Furthermore, the data does not demonstrate significant redshift evolution in the per-cluster tSZ power spectrum contribution, in contrast to self-similar model predictions.