Inferring IGM properties from EoR using neural networks

The redshifted 21-cm line of neutral hydrogen is a sensitive probe to investigate the different phases of the evolution of our Universe. The epoch of reionization marks a crucial phase transition in the high-redshift Universe, where the neutral Hydrogen in the IGM becomes completely ionized. Observations of this 21-cm line will directly enable us to map the young Universe, over a range of cosmic times and give us deep insight into the morphology of the ionization structures which were carved out by the first sources of light, as well as about the origin and evolution of these first generation sources. Detection of the HI 21-cm power spectrum is one of the primary science drivers of several ongoing and upcoming low-frequency radio interferometers, for example: LOFAR, MWA, HERA, SKA, etc. The connection between the IGM and the measured 21-cm power spectrum is very interesting, yet it's quite non-trivial. In this work, we use Artificial Neural Networks to develop a framework which will enable us to extract and constrain IGM properties, like bubble size distribution and reionization histories from 21cm power spectrum measurements at a single redshift.