期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 430, 期 4, 页码 3054-3069出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stt110
关键词
cosmic background radiation; cosmology: observations; cosmology: theory
资金
- NSF [AST-1009811]
- NASA ATP [NNX11AE07G]
- NASA Chandra Theory grant [GO213004B]
- Research Corporation by Yale University
- Division Of Astronomical Sciences
- Direct For Mathematical & Physical Scien [1009811] Funding Source: National Science Foundation
Future high-resolution, high-sensitivity Sunyaev-Zeldovich (SZ) observations of individual clusters will provide an exciting opportunity to answer specific questions about the dynamical state of the intracluster medium (ICM). In this paper we develop a new method that clearly shows the connection of the SZ signal with the underlying cluster model. We include relativistic temperature and kinematic corrections in the single-scattering approximation, allowing studies of hot clusters. In our approach, particular moments of the temperature and velocity field along the line-of-sight determine the precise spectral shape and morphology of the SZ signal. We illustrate how to apply our method to different cluster models, highlighting parameter degeneracies and instrumental effects that are important for interpreting future high-resolution SZ data. Our analysis shows that line-of-sight temperature variations can introduce significant biases in the derived SZ temperature and peculiar velocity. We furthermore discuss how the position of the SZ null is affected by the cluster's temperature and velocity structure. Our computations indicate that the SZ signal around the null alone is rather insensitive to different cluster models and that high-frequency channels add a large leverage in this respect. We also apply our method to recent high-sensitivity SZ data of the Bullet cluster, showing how the results can be linked to line-of-sight variations in the electron temperature. The tools developed here as part of SZPACK should be useful for analysing high-resolution SZ data and computing SZ maps from simulated clusters.
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