Machine learning based inference of EoR observations

by Mr. Hovav Lazare

BGU

at Astrophysics and Cosmology Seminar

Wed, 03 Jul 2024, 11:45
Sacta-Rashi Building for Physics (54), room 207

Abstract

EoR 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 from EoR redshifts, the Thomson scattering optical depth of CMB photons, and upper bounds on the neutral fraction at $z\sim 6$.

Created on 30-06-2024 by Zitrin, Adi (zitrin)
Updaded on 30-06-2024 by Zitrin, Adi (zitrin)