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Bayesian CMB foreground separation with a correlated log-normal model

Published online by Cambridge University Press:  01 July 2015

Niels Oppermann  and
Torsten A. Enßlin
Niels Oppermann
Affiliation:
Canadian Instittute for Theoretical Astrophysics, University of Toronto, 60 St. George Street, Toronto, ON, M5S 3H8, Canada email: niels@cita.utoronto.ca
Torsten A. Enßlin
Affiliation:
Max Planck Institute for Astrophysics, Karl-Schwarzschild-Straße 1, 85748 Garching, Germany email: ensslin@mpa-garching.mpg.de
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Abstract

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The extraction of foreground and CMB maps from multi-frequency observations relies mostly on the different frequency behavior of the different components. Existing Bayesian methods additionally make use of a Gaussian prior for the CMB whose correlation structure is described by an unknown angular power spectrum. We argue for the natural extension of this by using non-trivial priors also for the foreground components. Focusing on diffuse Galactic foregrounds, we propose a log-normal model including unknown spatial correlations within each component and cross-correlations between the different foreground components. We present case studies at low resolution that demonstrate the superior performance of this model when compared to an analysis with flat priors for all components.

Keywords

Methods: data analysismethods: statisticalISM: generalcosmic microwave background
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 16 - 18
Copyright
Copyright © International Astronomical Union 2015 

References

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