Speaker
Description
The redshifted 21-cm signal from neutral hydrogen (HI) is a powerful probe of the early universe, particularly the Epoch of Reionization (EoR). Detecting this signal is highly challenging, as it is both extremely weak and strongly obscured by astrophysical foregrounds. Consequently, one typically relies on the statistical properties of the 21-cm fluctuations to extract astrophysical and cosmological information. While most studies rely on the power spectrum, this two-point statistic fails to fully capture the information present in the highly non-Gaussian 21-cm signal. In this work, we argue that incorporating the next higher-order statistics, specifically the bispectrum, can significantly improve parameter inference estimation. We utilize machine learning methods to build a fast emulator for both statistics, enabling an efficient constraint of the neutral hydrogen fraction with Markov Chain Monte Carlo (MCMC) techniques. We combine the power spectrum and bispectrum to demonstrate that constraints on the neutral hydrogen fraction have significantly improved, leading to a more comprehensive understanding of the reionization process.
