Gaussian Process Reconstruction of Reionization History
Aditi Krishak, Dhiraj Kumar Hazra

TL;DR
This paper uses Gaussian process regression to reconstruct the reionization history from UV luminosity data, constraining the epoch of reionization with a model-independent approach and observational data.
Contribution
It introduces a model-independent Gaussian process method to reconstruct reionization history from UV luminosity data, challenging simple power-law models.
Findings
Reconstructed reionization history consistent with observational constraints.
Optical depth constrained to τ=0.052±0.001±0.002.
Duration between 10% and 90% ionization is approximately 2.05 units.
Abstract
We reconstruct the history of reionization using Gaussian process regression. Using the UV luminosity data compilation from Hubble Frontiers Fields we reconstruct the redshift evolution of UV luminosity density and thereby the evolution of the source term in the ionization equation. This model-independent reconstruction rules out single power-law evolution of the luminosity density but supports the logarithmic double power-law parametrization. We obtain reionization history by integrating ionization equations with the reconstructed source term. Using optical depth constraint from Planck Cosmic Microwave Background observation, measurement of UV luminosity function integrated till truncation magnitude of -17 and -15, and derived ionization fraction from high redshift quasar, galaxies and gamma-ray burst observations, we constrain the history of reionization. In the conservative case we…
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