Semi-blind Bayesian inference of CMB map and power spectrum

Flavien Vansyngel (1) , Benjamin D. Wandelt (1) , Jean-François Cardoso (1) , Karim Benabed (1)

1 IAP - Institut d'Astrophysique de Paris

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.

Mots clés: cosmic background radiation; methods: data analysis; methods: statistical

Domaines: Astrophysique [astro-ph]

Flavien Vansyngel, Benjamin D. Wandelt, Jean-François Cardoso, Karim Benabed. Semi-blind Bayesian inference of CMB map and power spectrum. Astronomy and Astrophysics - A&A, 2016, 588, pp.A113. ⟨10.1051/0004-6361/201424890⟩. ⟨hal-01321490⟩