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Bayesian model selection is a tool for deciding whether the introduction of a new parameter is warranted by the data. I argue that the usual sampling statistic significance tests for a null hypothesis can be misleading, since they do not take into account the information gained through the data, when updating the prior distribution to the posterior. In contrast, Bayesian model selection offers a quantitative implementation of Occam's razor. I introduce the Savage–Dickey density ratio, a computationally quick method to determine the Bayes factor of two nested models and hence perform model selection. As an illustration, I consider three key parameters for our understanding of the cosmological concordance model. By using Wilkinson Microwave Anisotropy Probe ( WMAP) 3-year data complemented by other cosmological measurements, I show that a non-scale-invariant spectral index of perturbations is favoured for any sensible choice of prior. It is also found that a flat universe is favoured with odds of 29:1 over non-flat models, and that there is strong evidence against a cold dark matter isocurvature component to the initial conditions which is totally (anti)correlated with the adiabatic mode (odds of about 2000:1), but that this is strongly dependent on the prior adopted. These results are contrasted with the analysis of WMAP 1-year data, which were not informative enough to allow a conclusion as to the status of the spectral index. In a companion paper, a new technique to forecast the Bayes factor of a future observation is presented. [ABSTRACT FROM AUTHOR]

We present an application of the fast Independent Component Analysis (fastica) to the Wilkinson Microwave Anisotropy Probe ( WMAP) three-year data with the goal of extracting the cosmic microwave background (CMB) signal. We evaluate the confidence of our results by means of Monte Carlo simulations including the CMB, foreground contaminations and instrumental noise specific to each WMAP frequency band. We perform a complete analysis involving all or a subset of the WMAP channels in order to select the optimal combination for CMB extraction, using the frequency scaling of the reconstructed component as a figure of merit. We find that the combination KQVW provides the best CMB frequency scaling, indicating that the low-frequency foreground contamination in Q, V and W bands is better traced by the emission in the K band. The CMB angular power spectrum is recovered up to the degree scale; it is consistent within errors for all WMAP channel combinations considered, and in close agreement with the WMAP three-year results. A power spectrum analysis is made of the sky map divided into two hemispheres that have been previously reported as showing evidence of an asymmetric ratio of power on large angular scales. We then confirm the findings of several previous works with independent techniques. [ABSTRACT FROM AUTHOR]

We perform a blind multicomponent analysis of the Wilkinson Microwave Anisotropy Probe ( WMAP) 1-yr foreground-cleaned maps using Spectral Matching Independent Component Analysis ( smica). We provide a new estimate of the cosmic microwave background (CMB) power spectrum as well as the amplitude of the CMB anisotropies across frequency channels. We show that the CMB anisotropies are compatible with temperature fluctuations as expected from the standard paradigm. The analysis also allows us to identify and separate a weak residual Galactic emission, present significantly in the Q band outside of the Kp2 1 mask limits, and mainly concentrated at low Galactic latitudes. We produce a map of this residual component by Wiener filtering using estimated parameters. The level of contamination of CMB data by this component is compatible with the WMAP-team estimation of foreground residual contamination. In addition, the multicomponent analysis allows us to estimate jointly the power spectrum of unresolved point-source emission. [ABSTRACT FROM AUTHOR]

Destriping methods for constructing maps of the cosmic microwave background (CMB) anisotropies have been investigated extensively in the literature. However, their error properties have been studied in less detail. Here we present an analysis of the effects of destriping errors on CMB power spectrum estimates forPlanck-like scanning strategies. Analytic formulae are derived for certain simple scanning geometries that can be rescaled to account for different detector noise. AssumingPlanck-like low-frequency noise, the noise power spectrum is accurately white at high multipoles. Destriping errors, though dominant at lower multipoles, are small in comparison to the cosmic variance. These results show that simple destriping map-making methods should be perfectly adequate for the analysis ofPlanckdata and support the arguments given in an earlier paper in favour of applying a fast hybrid power spectrum estimator to CMB data with realistic‘1/ f’ noise. [ABSTRACT FROM AUTHOR]