Data analysis of cosmic microwave background experiments

Statistics – Computation

Scientific paper

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Scientific paper

The cosmic microwave background (CMB) is a powerful tool for determining and constraining the fundamental properties of our universe. In this thesis we present various computational and statistical techniques used to analyze datasets from CMB experiments, and apply them to both simulated and actual datasets. The algorithms presented in this thesis perform a variety of tasks in relation to the goal of extracting scientific information from CMB data sets. The CMB anisotropy power spectrum is sensitive to numerous parameters that determine the evolutionary and large scale properties of our universe. Now that numerous experiments have mapped the CMB intensity fluctuations on overlapping regions of the sky it is important to ensure that the various experiments are indeed observing the same signal. We cross-correlate the cosmic microwave background temperature anisotropy maps from the WMAP, MAXIMA-I, and MAXIMA-II experiments. The results conclusively show that the three experiments not only display the same statistical properties of the CMB anisotropy, but also detect the same features wherever the observed sky areas overlap. We conclude that the contribution of systematic errors to these maps is negligible and that MAXIMA and WMAP have accurately mapped the cosmic microwave background anisotropy. Due to a quadrapole anisotropy at last scattering it is predicted that the CMB photons should be linearly polarized, and that the polarization intensity will be roughly an order of magnitude lower than the intensity fluctuations. Two computationally intensive methods for simulating the CMB polarization signal on the sky are presented. Now that CMB polarization experiments are currently producing data sets new algorithms for analyzing polarization time stream data must be developed and tested. We demonstrate how to generate simulations of a polarization experiment in the temporal domain and apply these simulations to the MAXIPOL case. We develop a maximum likelihood map making algorithm and apply it to the temporal simulations to generate simulated MAXIPOL temperature and polarization maximum likelihood maps, and the pixel noise correlation matrix. We show that the output from the map making code is in agreement with the expected results. (Abstract shortened by UMI.)

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