Reconstruction of the ionization history from 21cm maps with deep learning
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.