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Résumé :Within cosmology component separation is instrumental in disentangling information pertaining to the formation and evolution of the Universe itself from astrophysical information describing the Universes key constituents. Separating distinct, non-linear emission mechanisms within large-scale data sets is a complex problem which can benefit from a prior knowledge of said emission mechanisms. We present a new model fitting technique which exploits the sparse nature of these astrophysical emissions to provide more accurate model parameters in less computational time than the current state-of-the-art methodologies. This technique automatically identifies regions within the data with common properties, makes fast, initial parameter estimates and then refines these regional estimates to provide the global solution through a least squares optimisation which favours sparsity. We verify our method in the context of Planck simulation data.

Model-based component separation exploring sparsity

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