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The relation between mass and concentration in X-ray galaxy clusters at high redshift

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 نشر من قبل Stefania Amodeo
 تاريخ النشر 2016
  مجال البحث فيزياء
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 تأليف Stefania Amodeo




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Galaxy clusters are the most recent, gravitationally-bound products of the hierarchical mass accretion over cosmological scales. How the mass is concentrated is predicted to correlate with the total mass in the clusters halo, with systems at higher mass being less concentrated at given redshift and for any given mass, systems with lower concentration are found at higher redshifts. Through a spatial and spectral X-ray analysis, we reconstruct the total mass profile of 47 galaxy clusters observed with Chandra in the redshift range $0.4<z<1.2$, selected to have no major mergers, to investigate the relation between the mass and the dark matter concentration, and the evolution of this relation with redshift. The sample in exam is the largest one investigated so far at $z>0.4$, and is well suited to provide the first constraint on the concentration--mass relation at $z>0.7$ from X-ray analysis. Under the assumptions that the distribution of the X-ray emitting gas is spherically symmetric and in hydrostatic equilibrium, we combine the deprojected gas density and spectral temperature profiles through the hydrostatic equilibrium equation to recover the parameters that describe a NFW total mass distribution. The comparison with results from weak lensing analysis reveals a very good agreement both for masses and concentrations. Uncertainties are however too large to make any robust conclusion on the hydrostatic bias of these systems. The relation is well described by the form $c propto M^B (1+z)^C$, with $B=-0.50 pm 0.20$, $C=0.12 pm 0.61$ (at 68.3% confidence), it is slightly steeper than the one predicted by numerical simulations ($Bsim-0.1$) and does not show any evident redshift evolution. We obtain the first constraints on the properties of the concentration--mass relation at $z > 0.7$ from X-ray data, showing a reasonable good agreement with recent numerical predictions.



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