Non-parametric modeling of the intra-cluster gas using APEX-SZ bolometer imaging data

Details Non-parametric modeling of the intra-cluster gas using APEX-SZ bolometer imaging data Non-parametric modeling of the intra-cluster gas using APEX-SZ bolometer imaging data

2009-11-20

arxiv.org/abs/0911.3905v3

Astronomy and Astrophysics, Volume 519, A29 (2010)

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

Replaced with the published version; A&A 519, A29 (2010)

Scientific paper

10.1051/0004-6361/200913334

We demonstrate the usability of mm-wavelength imaging data obtained from the APEX-SZ bolometer array to derive the radial temperature profile of the hot intra-cluster gas out to radius r_500 and beyond. The goal is to study the physical properties of the intra-cluster gas by using a non-parametric de-projection method that is, aside from the assumption of spherical symmetry, free from modeling bias. We use publicly available X-ray imaging data from the XMM-Newton observatory and our Sunyaev-Zel'dovich Effect (SZE) imaging data from the APEX-SZ experiment at 150 GHz to de-project the density and temperature profiles for the relaxed cluster Abell 2204. We derive the gas density, temperature and entropy profiles assuming spherical symmetry, and obtain the total mass profile under the assumption of hydrostatic equilibrium. For comparison with X-ray spectroscopic temperature models, a re-analysis of the recent Chandra observation is done with the latest calibration updates. Using the non-parametric modeling we demonstrate a decrease of gas temperature in the cluster outskirts, and also measure the gas entropy profile. These results are obtained for the first time independently of X-ray spectroscopy, using SZE and X-ray imaging data. The contribution of the SZE systematic uncertainties in measuring T_e at large radii is shown to be small compared to the Chandra systematic spectroscopic errors. The upper limit on M_200 derived from the non-parametric method is consistent with the NFW model prediction from weak lensing analysis.

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