Speaker
Description
High redshift observations, such as the 21cm signal, luminosity functions, etc., carries immense potential to probe the formation and properties of the first galaxies, and beyond $\Lambda \mathrm{CDM}$ Cosmology. However, a complete statistical analysis of such observation is limited by the time consuming nature of simulators. Using machine learning based emulators we can overcome this obstacle, and produce fast and accurate realizations of these observables. Here we present two applications of this approach: (i) reproducing HERA constraints on X-ray luminosity in the early universe from the 21cm power spectrum, and re-evaluating these bounds in the presence of PopIII stars. (ii) Achieving new constraints on Fuzzy Dark Matter using luminosity functions, the Thomson scattering optical depth of CMB photons, and upper bounds on the neutral fraction at $z\sim 6$. In addition, we forecast that upcoming observations of the 21cm power spectrum can improve these bounds.
