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Component separation methods for CMB data
Component separation methods for CMB data
Measurements of the Cosmic Microwave Background (CMB) emission with increasingly high resolution and sensitivity are now becoming available, and even higher quality data are expected from the ongoing Planck mission and future experiments. Dealing with the Galactic foreground contamination, however, is still problematic, due to our poor knowledge of the physics of the Interstellar Medium at microwave frequencies. This contamination biases the CMB observations and needs to be removed before using the data for cosmological studies. In this thesis the problem of component separation for the CMB is considered and a highly focused study of a specific implementation of Independent Component Analysis (ICA), called FastICA, is presented. This algorithm has been used to perform a foreground analysis of the WMAP three and five-year data and subsequently to investigate the properties of the main sources of diffuse Galactic emission (e.g. synchrotron, dust and free-free emission). The foreground contamination in the WMAP data is quantified in terms of coupling coefficients between the data and various templates, which are observations of the sky emission at frequencies where only one physical component is likely to dominate. The coefficients have been used to extract the frequency spectra of the Galactic components, with particular attention paid to the free-free frequency spectrum. Our results favour the existence of a spectral ‘bump’, interpreted as a signature of emission by spinning dust grains in the Warm Ionised Medium, which spatially correlates with the Hα radiation used to trace the free-free emission. The same coupling coefficients have been used to clean the WMAP observations, which have then been further analysed using FastICA. This iterative step in the analysis provides a powerful tool for cleaning the CMB data of any residuals not traced by the adopted templates. In practice, it is a unique way to potentially reveal new physical emission components. In this way, we detected a residual spatially concentrated emission component around the Galactic center, consistent with the so-called WMAP Haze. In order to take into account the actual spatial properties of the Galactic foreground emission, we proposed an analysis of theWMAP data on patches of the sky, both using FastICA and the Internal Linear Combination (ILC). Since the temperature power spectrum is reasonably insensitive to the fine details of the foreground corrections except on the largest scales (low l), the two methods are compared by means of non-Gaussianity tests, used to trace the presence of possible residuals. While the performance of FastICA improves only for particular cases with a small number of regions, the ILC CMB estimation generally ameliorates significantly if the number of patches is increased. Moreover, FastICA plays a key role in establishing a partitioning that realistically traces the features of the sky, a requirement we have shown to be paramount for a successful regional analysis.
CMB, foregrounds, component separation methods
Bottino, Maria-Paola
2010
Englisch
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Bottino, Maria-Paola (2010): Component separation methods for CMB data. Dissertation, LMU München: Fakultät für Physik
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Abstract

Measurements of the Cosmic Microwave Background (CMB) emission with increasingly high resolution and sensitivity are now becoming available, and even higher quality data are expected from the ongoing Planck mission and future experiments. Dealing with the Galactic foreground contamination, however, is still problematic, due to our poor knowledge of the physics of the Interstellar Medium at microwave frequencies. This contamination biases the CMB observations and needs to be removed before using the data for cosmological studies. In this thesis the problem of component separation for the CMB is considered and a highly focused study of a specific implementation of Independent Component Analysis (ICA), called FastICA, is presented. This algorithm has been used to perform a foreground analysis of the WMAP three and five-year data and subsequently to investigate the properties of the main sources of diffuse Galactic emission (e.g. synchrotron, dust and free-free emission). The foreground contamination in the WMAP data is quantified in terms of coupling coefficients between the data and various templates, which are observations of the sky emission at frequencies where only one physical component is likely to dominate. The coefficients have been used to extract the frequency spectra of the Galactic components, with particular attention paid to the free-free frequency spectrum. Our results favour the existence of a spectral ‘bump’, interpreted as a signature of emission by spinning dust grains in the Warm Ionised Medium, which spatially correlates with the Hα radiation used to trace the free-free emission. The same coupling coefficients have been used to clean the WMAP observations, which have then been further analysed using FastICA. This iterative step in the analysis provides a powerful tool for cleaning the CMB data of any residuals not traced by the adopted templates. In practice, it is a unique way to potentially reveal new physical emission components. In this way, we detected a residual spatially concentrated emission component around the Galactic center, consistent with the so-called WMAP Haze. In order to take into account the actual spatial properties of the Galactic foreground emission, we proposed an analysis of theWMAP data on patches of the sky, both using FastICA and the Internal Linear Combination (ILC). Since the temperature power spectrum is reasonably insensitive to the fine details of the foreground corrections except on the largest scales (low l), the two methods are compared by means of non-Gaussianity tests, used to trace the presence of possible residuals. While the performance of FastICA improves only for particular cases with a small number of regions, the ILC CMB estimation generally ameliorates significantly if the number of patches is increased. Moreover, FastICA plays a key role in establishing a partitioning that realistically traces the features of the sky, a requirement we have shown to be paramount for a successful regional analysis.