The Concentration-Mass Relation of Massive, Dynamically Relaxed Galaxy Clusters: Agreement Between Observations and $Λ$CDM Simulations

Kavli Affiliate: R. Glenn Morris

| First 5 Authors: Elise Darragh-Ford, Adam B. Mantz, Elena Rasia, Steven W. Allen, R. Glenn Morris

| Summary:

The relationship linking a galaxy cluster’s total mass with the concentration
of its mass profile and its redshift is a fundamental prediction of the Cold
Dark Matter (CDM) paradigm of cosmic structure formation. However, confronting
those predictions with observations is complicated by the fact that simulated
clusters are not representative of observed samples where detailed mass profile
constraints are possible. In this work, we calculate the
Symmetry-Peakiness-Alignment (SPA) morphology metrics for maps of X-ray
emissivity from THE THREE HUNDRED project hydrodynamical simulations of galaxy
clusters at four redshifts, and thereby select a sample of morphologically
relaxed, simulated clusters, using observational criteria. These clusters have
on average earlier formation times than the full sample, confirming that they
are both morphologically and dynamically more relaxed than typical. We
constrain the concentration-mass-redshift relation of both the relaxed and
complete sample of simulated clusters, assuming power-law dependences on mass
($kappa_m$) and $1+z$ ($kappa_zeta$), finding $kappa_m = -0.12 pm 0.07$
and $kappa_zeta = -0.27 pm 0.19$ for the relaxed subsample. From an
equivalently selected sample of massive, relaxed clusters observed with ${it
Chandra}$, we find $kappa_m = -0.12 pm 0.08$ and $kappa_zeta = -0.48 pm
0.19$, in good agreement with the simulation predictions. The simulated and
observed samples also agree well on the average concentration at a pivot mass
and redshift providing further validation of the $Lambda$CDM paradigm in the
properties of the largest gravitationally collapsed structures observed. This
also represents the first clear detection of decreasing concentration with
redshift, a longstanding prediction of simulations, in data.

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