SZ and CMB reconstruction using generalized morphological component analysis
In the last decade, the study of cosmic microwave background (CMB) data has become one of the most powerful tools for studying and understanding the Universe. More precisely, measuring the CMB power spectrum leads to the estimation of most cosmological parameters. Nevertheless, accessing such precio...
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| Veröffentlicht in: | Statistical methodology 2008-07, Vol.5 (4), p.307-317 |
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| creator | Bobin, J. Moudden, Y. Starck, J.-L. Fadili, J. Aghanim, N. |
| description | In the last decade, the study of cosmic microwave background (CMB) data has become one of the most powerful tools for studying and understanding the Universe. More precisely, measuring the CMB power spectrum leads to the estimation of most cosmological parameters. Nevertheless, accessing such precious physical information requires extracting several different astrophysical components from the data. Recovering those astrophysical sources (CMB, Sunyaev–Zel’dovich clusters, galactic dust) thus amounts to a component separation problem which has already led to an intensive activity in the field of CMB studies. In this paper, we introduce a new sparsity-based component separation method coined Generalized Morphological Component Analysis (GMCA). The GMCA approach is formulated in a Bayesian
maximum a posteriori (MAP) framework. Numerical results show that this new source recovery technique performs well compared to state-of-the-art component separation methods already applied to CMB data. |
| doi_str_mv | 10.1016/j.stamet.2007.10.003 |
| format | Article |
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| ispartof | Statistical methodology, 2008-07, Vol.5 (4), p.307-317 |
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| subjects | Astrophysics Blind component separation Computer Science Cosmic microwave background Cosmology and Extra-Galactic Astrophysics Morphological component analysis Morphological diversity Physics Sciences of the Universe Signal and Image Processing Sparse overcomplete representations Sparsity Sunyaev–Zel’dovich |
| title | SZ and CMB reconstruction using generalized morphological component analysis |
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