Speaker
Description
Cosmology is currently experiencing a data-rich era, with even more data expected in the coming decade. Intensity mapping of 21 cm neutral hydrogen emission offers a promising avenue for mapping vast volumes of the Universe with high redshift resolution, aiding in the study of large-scale structure and cosmological parameter estimation. However, extracting cosmological information from these datasets poses challenges, including significant foreground contamination, instrumental effects, and the loss of information inherent in using two-point statistics in highly non-Gaussian fields. This ongoing PhD project aims to explore the potential of higher-order statistics—such as Minkowski functionals, peak counts, and probability distribution functions—combined with machine learning techniques for cosmological inference from 21 cm data. The research is organized into three main topics: (i) assessing the robustness of these statistics against foreground residuals and instrumental imperfections; (ii) quantifying their cosmological information content using N-body simulations through a simulation-based inference approach (SBI); and (iii) developing an emulator for higher-order statistics and comparing different cosmological inference methodologies. The results and tools generated from this project will significantly contribute to the scientific preparation for future 21 cm experiments, such as SKA, the largest experiment of its kind currently under construction, and BINGO, a radio telescope being developed in Brazil.