Joint reconstruction of galaxy clusters from gravitational lensing and thermal gas: I. outline of a non-parametric method
We present a method to estimate the lensing potential from massive galaxy clusters for given observational X-ray data. The concepts developed and applied in this work can easily be combined with other techniques to infer the lensing potential, e.g. weak gravitational lensing or galaxy kinematics, to...
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| Main Authors: | , , , , |
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| Format: | Article (Journal) Chapter/Article |
| Language: | English |
| Published: |
19 Apr 2013
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| In: |
Arxiv
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| Online Access: | Verlag, Volltext: http://arxiv.org/abs/1304.5443
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| Author Notes: | Sara Konrad, Charles L. Majer, Sven Meyer, Eleonora Sarli, and Matthias Bartelmann |
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| 245 | 1 | 0 | |a Joint reconstruction of galaxy clusters from gravitational lensing and thermal gas |b I. outline of a non-parametric method |c Sara Konrad, Charles L. Majer, Sven Meyer, Eleonora Sarli, and Matthias Bartelmann |
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| 520 | |a We present a method to estimate the lensing potential from massive galaxy clusters for given observational X-ray data. The concepts developed and applied in this work can easily be combined with other techniques to infer the lensing potential, e.g. weak gravitational lensing or galaxy kinematics, to obtain an overall best fit model for the lensing potential. After elaborating on the physical details and assumptions the method is based on, we explain how the numerical algorithm itself is implemented with a Richardson-Lucy algorithm as a central part. Our reconstruction method is tested on simulated galaxy clusters with a spherically symmetric NFW density profile filled with gas in hydrostatic equilibrium. We describe in detail how these simulated observational data sets are created and how they need to be fed into our algorithm. We test the robustness of the algorithm against small parameter changes and estimate the quality of the reconstructed lensing potentials. As it turns out we achieve a very high degree of accuracy in reconstructing the lensing potential. The statistical errors remain below 2.0% whereas the systematical error does not exceed 1.0%. | ||
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