Masters of Science / Upcoming and ongoing 21cm surveys, such as the Square Kilometre Array (SKA), Hydrogen
Epoch of Reionization Array (HERA) and Low Frequency Array (LOFAR), will enable imaging
of the neutral hydrogen distribution on cosmological scales in the early Universe. These
experiments are expected to generate huge imaging datasets that will encode more information
than the power spectrum. This provides an alternative unique way to constrain the astrophysical
and cosmological parameters, which might break the degeneracies in the power spectral analysis.
The global history of reionization remains fairly unconstrained. In this thesis, we explore
the viability of directly using the 21cm images to reconstruct and constrain the reionization
history. Using Convolutional Neural Networks (CNN), we create a fast estimator of the global
ionization fraction from the 21cm images as produced by our Large Semi-numerical Simulation
(SimFast21). Our estimator is able to efficiently recover the ionization fraction (xHII) at several
redshifts, z = 7; 8; 9; 10 with an accuracy of 99% as quantified by the coefficient of determination
R2 without being given any additional information about the 21cm maps. This approach,
contrary to estimations based on the power spectrum, is model independent. When adding the
thermal noise and instrumental effects from these 21cm arrays, the results are sensitive to the
foreground removal level, affecting the recovery of high neutral fractions. We also observe
similar trend when combining all redshifts but with an improved accuracy. Our analysis can
be easily extended to place additional constraints on other astrophysical parameters such as the
photon escape fraction. This work represents a step forward to extract the astrophysical and
cosmological information from upcoming 21cm surveys.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uwc/oai:etd.uwc.ac.za:11394/7243 |
| Date | January 2020 |
| Creators | Mangena |
| Contributors | Santos, Mario, Hassan, Sultan |
| Publisher | University of the Western Cape |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Rights | University of the Western Cape |
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