State of the Universe

Probing the Cosmic Reionization using an efficient SCRIPT

by Dr. Barun Maity (NCRA, Pune)

Tuesday, January 3, 2023 from to (Asia/Kolkata)
at A304 and Zoom : MEETING ID: 827 0473 4378 PASSCODE: 311436
According to the standard model of cosmology, the universe was mostly ionized and hot at very early stages. Then it cooled down with time and became predominantly neutral around 380,000 years after birth. Reionization is the era when the universe is again ionized by the photons coming from the first luminous sources. This is still one of the least understood phases in the evolutionary history of the Universe and is also known as one of the final frontiers in modern cosmology. The ionization and thermal state of the intergalactic medium (IGM) during the epoch of reionization has been of interest in recent times because of their close connection to the first stars. We try to constrain the thermal and ionization history of the universe using a semi-numerical photon conserving model SCRIPT and a variety of observables like UV luminosity function, low-density IGM temperatures, CMB scattering optical depth, etc. We study the consequences of physical effects like inhomogeneous recombination and radiative feedback on the reionization phenomena, which is necessary for accurate modelling. We find that the model parameters are reasonably well constrained which can give insights into the reionization timeline. The bounds will certainly be improved with more observational data coming in near future. As we track the inhomogeneities in the medium, we can also compute the large-scale 21cm power spectra which quantifies the fluctuations in the neutral hydrogen field. We check the prospects of 21cm power spectra as a tracer of reionization. Our study involves creating a mock data set corresponding to the upcoming SKA-Low, followed by a Bayesian inference method to constrain the model parameters. In particular, we explore in detail whether the inferred parameters are unbiased with respect to the inputs used for the mock and if the inferences are insensitive to the resolution of the simulation. We find that the model is reasonably successful on both fronts.