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arXiv · 0906.5071

The mass and anisotropy profiles of galaxy clusters from the projected phase space density: testing the method on simulated data

Radoslaw Wojtak·Ewa L. Lokas·Gary A. Mamon·Stefan Gottloeber
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Abstract

We present a new method of constraining the mass and velocity anisotropy profiles of galaxy clusters from kinematic data. The method is based on a model of the phase space density which allows the anisotropy to vary with radius between two asymptotic values. The characteristic scale of transition between these asymptotes is fixed and tuned to a typical anisotropy profile resulting from cosmological simulations. The model is parametrized by two values of anisotropy, at the centre of the cluster and at infinity, and two parameters of the NFW density profile, the scale radius and the scale mass. In order to test the performance of the method in reconstructing the true cluster parameters we analyze mock kinematic data for 20 relaxed galaxy clusters generated from a cosmological simulation of the standard LCDM model. We use Bayesian methods of inference and the analysis is carried out following the Markov Chain Monte Carlo approach. The parameters of the mass profile are reproduced quite well, but we note that the mass is typically underestimated by 15 percent, probably due to the presence of small velocity substructures. The constraints on the anisotropy profile for a single cluster are in general barely conclusive. Although the central asymptotic value is determined accurately, the outer one is subject to significant systematic errors caused by substructures at large clustercentric distance. The anisotropy profile is much better constrained if one performs joint analysis of at least a few clusters. In this case it is possible to reproduce the radial variation of the anisotropy over two decades in radius inside the virial sphere.

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Radoslaw Wojtak, Ewa L. Lokas, Gary A. Mamon, Stefan Gottloeber. 2009-06-27. The mass and anisotropy profiles of galaxy clusters from the projected phase space density: testing the method on simulated data. https://doi.org/10.1111/j.1365-2966.2009.15312.x

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