Volume 588, April 2016
|Number of page(s)||12|
|Section||Cosmology (including clusters of galaxies)|
|Published online||28 March 2016|
Semi-blind Bayesian inference of CMB map and power spectrum
1 Sorbonne Universités, UPMC Univ Paris 06, UMR 7095, Institut d’Astrophysique de Paris, 75014 Paris, France
2 CNRS, UMR 7095, Institut d’Astrophysique de Paris, 75014 Paris, France
3 Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
4 Department of Astronomy, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
5 Laboratoire Traitement et Communication de l’Information, CNRS, UMR 5141 and Télécom ParisTech, 46 rue Barrault, 75634 Paris Cedex 13, France
6 APC, AstroParticule et Cosmologie, Université Paris Diderot, 75013 Diderot, France
Received: 31 August 2014
Accepted: 24 January 2016
We present a new blind formulation of the cosmic microwave background (CMB) inference problem. The approach relies on a phenomenological model of the multifrequency 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 had 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, spectral mismatch independent component analysis (SMICA), and internal linear combination (ILC), and discuss possible future extensions.
Key words: cosmic background radiation / methods: data analysis / methods: statistical
© ESO, 2016
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