2011
DOI: 10.1103/physrevd.84.063005
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Framework for performance forecasting and optimization of CMBB-mode observations in the presence of astrophysical foregrounds

Abstract: We present a formalism for performance forecasting and optimization of future cosmic microwave background (CMB) experiments. We implement it in the context of nearly full sky, multifrequency, B-mode polarization observations, incorporating statistical uncertainties due to the CMB sky statistics, instrumental noise, as well as the presence of the foreground signals. We model the effects of a subtraction of these using a parametric maximum likelihood technique and optimize the instrumental configuration with pre… Show more

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Cited by 29 publications
(32 citation statements)
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“…Our approach is based on the parametrization of the emission laws for two important astrophysical contaminants, namely dust and synchrotron [44,[47][48][49]. 3 To estimate the impact of performing component separation with a given instrument, we use a parametric maximum-likelihood component-separation approach, as implemented in Ref.…”
Section: Formalismmentioning
confidence: 99%
“…Our approach is based on the parametrization of the emission laws for two important astrophysical contaminants, namely dust and synchrotron [44,[47][48][49]. 3 To estimate the impact of performing component separation with a given instrument, we use a parametric maximum-likelihood component-separation approach, as implemented in Ref.…”
Section: Formalismmentioning
confidence: 99%
“…As we anticipated, we will exploit the publicly available code FGBuster which represents an implementation of parameter fitting in foreground estimation and removal for CMB experiments. We review here very briefly the corresponding formalism used for the computation of the uncertainties after component separation, which is based on the parametric maximum likelihood approach [40,[56][57][58], and is the basis of the FGBuster implementation. In the presence of multi-component emissions contributing to the signal measured on a given sky pixel p, we can write d p = As p + n p , (4.1)…”
Section: Uncertainties From Foreground Cleaningmentioning
confidence: 99%
“…These uncertainty terms add extra power to the CMB map. We forecast these contributions following [56].…”
Section: Uncertainties From Foreground Cleaningmentioning
confidence: 99%
“…Foreground cleaning is assumed to be performed by a parametric maximum-likelihood approach (Brandt et al 1994;Eriksen et al 2006;Stompor et al 2009), in which the frequency dependence of each foreground component (which may vary spatially) is estimated from multifrequency observations and then used to construct a cleaned CMB map. The noise and foreground residuals in the resulting CMB map (Errard et al 2011;Errard & Stompor 2012) are propagated through the rest of the forecast (Verde et al 2006), and projected constraints on cosmological parameters are obtained with a power-spectrum-based Fisher formalism.…”
Section: Forecast Methodologymentioning
confidence: 99%