Semi-blind Bayesian inference of CMB map and power spectrum

Abstract

We present a new blind formulation of the Cosmic Microwave Background (CMB) inference problem. The approach relies on a phenomenological model of the multi-frequency microwave sky without the need for physical models of the individual components. For all-sky and high resolution data, it unifies parts of the analysis that have previously been treated separately, such as component separation and power spectrum inference. We describe an efficient sampling scheme that fully explores the component separation uncertainties on the inferred CMB products such as maps and/or power spectra. External information about individual components can be incorporated as a prior giving a flexible way to progressively and continuously introduce physical component separation from a maximally blind approach. We connect our Bayesian formalism to existing approaches such as Commander, SMICA and ILC, and discuss possible future extensions.

11 pages, 9 figures

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• Needlet thresholding methods in component separation
• Data driven foreground clustering approach to component separation in multifrequency CMB experiments: A new Planck CMB map
• Non-parametric maximum likelihood component separation for CMB polarization data
• The CMB angular power spectrum via component separation: a study on Planck data