The effect of AGN feedback on the X-ray morphologies of clusters: Simulations vs. observations

Abstract

Clusters of galaxies probe the large-scale distribution of matter and are a useful tool to test the cosmological models by constraining cosmic structure growth and the expansion of the Universe. It is the scaling relations between mass observables and the true mass of a cluster through which we obtain the cosmological constraints by comparing to theoretical cluster mass functions. These scaling relations are, however, heavily influenced by cluster morphology. The presence of the slight tension in recent cosmological constraints on Ω m and σ 8 based on the CMB and clusters has boosted the interests in looking for possible sources for the discrepancy. Therefore we study here the effect of active galactic nucleus (AGN) feedback as one of the major mechanisms modifying the cluster morphology influencing scaling relations. It is known that AGN feedback injects energies up to 10 62 erg into the intracluster medium, controls the heating and cooling of a cluster, and re-distributes cold gas from the centre to outer radii. We have also learned that cluster simulations with AGN feedback can reproduce observed cluster properties, for example, the X-ray luminosity, temperature, and cooling rate at the centre better than without the AGN feedback. In this paper using cosmological hydrodynamical simulations we investigate how the AGN feedback changes the X-ray morphology of the simulated systems, and compare this to the observed Representative XMM-Newton Cluster Structure Survey (REXCESS) clusters. We apply two substructure measures, centre shifts (w) and power ratios (e.g. P 3 /P 0 ), to characterise the cluster morphology, and find that our simulated clusters are more substructured than the observed clusters based on the values of w and P 3 /P 0 . We also show that the degree of this discrepancy is affected by the inclusion of AGN feedback. While the clusters simulated with the AGN feedback are in much better agreement with the REXCESS L X −T relation, they are also more substructured, which increases the tension with observations. When classified as non-relaxed or relaxed according to their w and P 3 /P 0 values, we find that there are no relaxed clusters in the simulations with the AGN feedback. This suggests that not only global cluster properties, like L X and T, and radial profiles should be used to compare and to calibrate simulations with observations, but also substructure measures like centre shifts and power ratios. Finally, we discuss what changes in the simulations might ease the tension with observational constraints on these quantities.

The Three Hundred project: quest of clusters of galaxies morphology and dynamical state through Zernike polynomials

“…Hydrodynamical simulations also allowed to validate these parameters with mock X-ray maps (see e.g. Poole et al 2006;Böhringer et al 2010;Morandi et al 2013;Rasia et al 2013;Weißmann et al 2013;Chon et al 2016;Chen et al 2019;Ansarifard et al 2020;Cao et al 2020).…”

The Halo Mass–Temperature Relation for Clusters, Groups, and Galaxies

“…These simulations showed that AGN feedback can successfully suppress cooling in cosmological simulations and isolated systems. Sijacki & Springel (2006), Chon et al (2016), and Rosito et al (2021) also found that AGN feedback is able to break self-similarity. The contributions of AGN and supernovae feedback, stellar mass loss, thermal condition, and/or cosmic rays vary depending on the mass of the source, increasing from clusters to low-mass systems.…”

Disturbed galaxy clusters are more abundant in an X-ray volume-limited sample

“…They are well tested for X-ray observations and simulations (see e.g. Böhringer et al (2010), , Mahdavi et al (2013), Rasia et al (2013), Chon et al (2016)) and have been shown to provide very useful diagnostics.…”

“…both parameters by r 500 instead of their available fraction, and they are represented by upward arrows. As in Chon et al ( , 2016, we defined three boundaries to classify the clusters into three distinct categories: disturbed (w > 0.006 or P 3 > 2×10 −7 ), intermediate (w < 0.006 and 7×10 −8 < P 3 < 2×10 −7 ), and relaxed (w < 0.006 and P 3 < 7×10 −8 ). The number of clusters based on this classification is listed in Table 1 under the heading VLS, and the same information is given for two flux-limited samples that are constructed from ReVols, namely FLS1 and FLS2.…”