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Title: Seeing the first light : a study of the Dark and Dim Ages
Author: Chapman, Emma Olivia
ISNI:       0000 0004 5363 8841
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2014
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The Epoch of Reionization (EoR) represents a major phase shift in the history of our Universe. After a long, dark, period of expansion and cooling, the first ionizing sources began to ionize the surrounding hydrogen atoms. These bubbles of ionized hydrogen grew and overlapped until eventually the Universe was mostly ionized. The EoR remains a largely mysterious and unconstrained era but, since the hyperfine transition of the hydrogen atom produces a photon of wavelength 21-cm, how the distribution of this 21-cm radiation changes over redshift can be measured by current generation radio telescopes such as the Low Frequency Array (LOFAR). This thesis addresses four problems within the EoR: Firstly, a statistical detection of the EoR is not a straight-forward task due to the considerable contribution of 21-cm radiation from non-EoR sources. In this thesis I introduce two statistical methods for EoR foreground removal based on statistical independence and sparsity. Secondly, the peculiar velocity of a hydrogen atom upon emission of a 21-cm photon has an effect on the 21-cm intensity and the frequency at which we observe the photon. I adapt a semi-analytic code to output a multi-frequency observation in redshift space, a `light cone', fully incorporating the peculiar velocities of the atoms. Thirdly, recent publications suggest that there is an area of Fourier space where the EoR signal is the dominant contribution, suggesting that statistical analysis could be carried out within that region only, relieving the need for foreground subtraction methods. I consider a physically-motivated foreground model and show that the supposed EoR window is completely compromised. Finally, I apply my foreground subtraction methods to the first LOFAR-EoR data. I find that GMCA not only removes the foregrounds impressively well for such raw data but actually acts as a powerful tool for the identification of systematics within the data.
Supervisor: Abdalla, F. B. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available