Dr Arianna Rizzieri

Abstract:

The variation of the physical conditions across the three dimensions of our Galaxy is a major source of complexity for the modelling of the foreground signal facing the cosmic microwave background (CMB). In the present work, we demonstrate that the spin-moment expansion formalism provides a powerful framework to model and understand this complexity, and we put special focus on the effects that arise from variations of the physical conditions along each line of sight on the sky. We performed the first application of the moment expansion to reproduce a thermal dust model largely used by the CMB community, demonstrating its power as a minimal tool to compress, understand, and model the information contained within any foreground model. Furthermore, we used this framework to produce new models of thermal dust emission containing the maximal amount of complexity allowed by the current data while remaining compatible with the observed angular power spectra by the Planck mission. By assessing the impact of these models on the performance of component separation methodologies, we conclude that the additional complexity contained within the third dimension could represent a significant challenge for future CMB experiments and that different component separation approaches are sensitive to different properties of the moments.

Abstract:

We implement a simple, main beam correction in the maximum-likelihood, parametric component separation approach, which allows on accounting for different beam widths of input maps at different frequencies without any preprocessing. We validate the approach on full-sky and cut-sky simulations and discuss the importance and impact of the assumptions and simplifications. We find that, in the cases when the underlying sky model is indeed parametric, the method successfully recovers component spectral parameters and component maps at the predefined resolution. The improvement on the precision of the estimated spectral parameters is found to be minor due to the redness of the foreground angular spectra, however the method is potentially more accurate, in particular if the foreground properties display strong, spatial variability, as it does not assume commutation of the beam smoothing and mixing matrix operators. The method permits a reconstruction of the cosmic microwave background map with a resolution significantly superior to that of the lowest resolution map used in the analysis and with the nearly optimal noise level, facilitating exploitation of the cosmological information contained on angular scales, which would be otherwise inaccessible. The method preserves all the advantages of a pixel-domain implementation of the parametric approach, and, as it deals with the beams in the harmonic domain, it can also straightforwardly account for spatially stationary map-domain noise correlations.

Abstract:

High fidelity separation of astrophysical foreground contributions from the cosmic microwave background (CMB) signal has been recognized as one of the main challenges of modern CMB data analysis, one that needs to be addressed in a robust way to ensure that the next generation of CMB polarization experiments lives up to its promise. In this work we consider the nonparametric maximum likelihood CMB cleaning approach recently proposed by some of the authors that has been shown to match the performance of standard parametric techniques for simple foreground models, while superseding it in cases where the foregrounds do not exhibit a simple frequency dependence. We present a new implementation of the method in pixel space, extending its functionalities to account for spatial variability of the properties of the foregrounds. We describe the algorithmic details of our approach and its validation against the original code as well as the parametric method for various experimental setups and different models of the foreground components. We argue that the method provides a compelling alternative to other state-of-the-art techniques.