Joint Bayesian Component Separation And Cmb Power Spectrum Estimation

Details Joint Bayesian Component Separation And Cmb Power Spectrum Estimation Joint Bayesian Component Separation And Cmb Power Spectrum Estimation

Dec 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007aas...211.9001d&link_type=abstract

American Astronomical Society, AAS Meeting #211, #90.01; Bulletin of the American Astronomical Society, Vol. 39, p.883

Statistics

Computation

2

Scientific paper

We have implemented an exact, flexible, and computationally efficient algorithm for joint component separation and CMB power spectrum estimation, building on a Gibbs framework. Given a parametric model of the foreground signals, we obtain the exact joint foreground-CMB posterior distribution, and therefore all marginal distributions such as the CMB power spectrum or foreground spectral index posteriors. We have extensively verified the code using simulations for both WMAP-like and Planck-like data. We present results of the actual 3-year WMAP temperature data, verifying the WMAP power spectrum up to l=50. The method is likely to be optimal for Planck data up to l 200.

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