No Arabic abstract
Powerful constraints on theories can already be inferred from existing CMB anisotropy data. But performing an exact analysis of available data is a complicated task and may become prohibitively so for upcoming experiments with gtrsim10^4 pixels. We present a method for approximating the likelihood that takes power spectrum constraints, e.g., ``band-powers, as inputs. We identify a bias which results if one approximates the probability distribution of the band-power errors as Gaussian---as is the usual practice. This bias can be eliminated by using specific approximations to the non-Gaussian form for the distribution specified by three parameters (the maximum likelihood or mode, curvature or variance, and a third quantity). We advocate the calculation of this third quantity by experimenters, to be presented along with the maximum-likelihood band-power and variance. We use this non-Gaussian form to estimate the power spectrum of the CMB in eleven bands from multipole moment ell = 2 (the quadrupole) to ell=3000 from all published band-power data. We investigate the robustness of our power spectrum estimate to changes in these approximations as well as to selective editing of the data.
We present a simple way of coding and compressing the data on board the Planck instruments (HFI and LFI) to address the problem of the on board data reduction. This is a critical issue in the Planck mission. The total information that can be downloaded to Earth is severely limited by the telemetry allocation. This limitation could reduce the amount of diagnostics sent on the stability of the radiometers and, as a consequence, curb the final sensitivity of the CMB anisotropy maps. Our proposal to address this problem consists in taking differences of consecutive circles at a given sky pointing. To a good approximation, these differences are independent of the external signal, and are dominated by thermal (white) instrumental noise. Using simulations and analytical predictions we show that high compression rates, $c_r simeq 10$, can be obtained with minor or zero loss of CMB sensitivity. Possible effects of digital distortion are also analized. The proposed scheme allows for flexibility to optimize the relation with other critical aspects of the mission. Thus, this study constitutes an important step towards a more realistic modeling of the final sensitivity of the CMB temperature anisotropy maps.
We aim to present a tutorial on the detection, parameter estimation and statistical analysis of compact sources (far galaxies, galaxy clusters and Galactic dense emission regions) in cosmic microwave background observations. The topic is of great relevance for current and future cosmic microwave background missions because the presence of compact sources in the data introduces very significant biases in the determination of the cosmological parameters that determine the energy contain, origin and evolution of the universe and because compact sources themselves provide us with important information about the large scale structure of the universe.
Scalar wavelets have been used extensively in the analysis of Cosmic Microwave Background (CMB) temperature maps. Spin needlets are a new form of (spin) wavelets which were introduced in the mathematical literature by Geller and Marinucci (2008) as a tool for the analysis of spin random fields. Here we adopt the spin needlet approach for the analysis of CMB polarization measurements. The outcome of experiments measuring the polarization of the CMB are maps of the Stokes Q and U parameters which are spin 2 quantities. Here we discuss how to transform these spin 2 maps into spin 2 needlet coefficients and outline briefly how these coefficients can be used in the analysis of CMB polarization data. We review the most important properties of spin needlets, such as localization in pixel and harmonic space and asymptotic uncorrelation. We discuss several statistical applications, including the relation of angular power spectra to the needlet coefficients, testing for non-Gaussianity on polarization data, and reconstruction of the E and B scalar maps.
Delensing is an increasingly important technique to reverse the gravitational lensing of the cosmic microwave background (CMB) and thus reveal primordial signals the lensing may obscure. We present a first demonstration of delensing on Planck temperature maps using the cosmic infrared background (CIB). Reversing the lensing deflections in Planck CMB temperature maps using a linear combination of the 545 and 857GHz maps as a lensing tracer, we find that the lensing effects in the temperature power spectrum are reduced in a manner consistent with theoretical expectations. In particular, the characteristic sharpening of the acoustic peaks of the temperature power spectrum resulting from successful delensing is detected at a significance of 16$rm{sigma}$, with an amplitude of $A_{rm{delens}} = 1.12 pm 0.07$ relative to the expected value of unity. This first demonstration on data of CIB delensing, and of delensing techniques in general, is significant because lensing removal will soon be essential for achieving high-precision constraints on inflationary B-mode polarization.
We describe an efficient and exact method that enables global Bayesian analysis of cosmic microwave background (CMB) data. The method reveals the joint posterior density (or likelihood for flat priors) of the power spectrum $C_ell$ and the CMB signal. Foregrounds and instrumental parameters can be simultaneously inferred from the data. The method allows the specification of a wide range of foreground priors. We explicitly show how to propagate the non-Gaussian dependency structure of the $C_ell$ posterior through to the posterior density of the parameters. If desired, the analysis can be coupled to theoretical (cosmological) priors and can yield the posterior density of cosmological parameter estimates directly from the time-ordered data. The method does not hinge on special assumptions about the survey geometry or noise properties, etc. It is based on a Monte Carlo approach and hence parallelizes trivially. No trace or determinant evaluations are necessary. The feasibility of this approach rests on the ability to solve the systems of linear equations which arise. These are of the same size and computational complexity as the map-making equations. We describe a pre-conditioned conjugate gradient technique that solves this problem and demonstrate in a numerical example that the computational time required for each Monte Carlo sample scales as $n_p^{3/2}$ with the number of pixels $n_p$. We test our method using the COBE-DMR data and explore the non-Gaussian joint posterior density of the COBE-DMR $C_ell$ in several projections.