Efficient Modelling of Lyman-α opacity fluctuations during late EoR

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Efficient Modelling of Lyman-α opacity fluctuations during late EoR

Authors

Barun Maity, Frederick Davies, Prakash Gaikwad

Abstract

The Lyman-$\alpha$ forest opacity fluctuations observed from high redshift quasar spectra have been proven to be extremely successful in order to probe the late phase of the reionization epoch. For ideal modeling of these opacity fluctuations, one of the main challenges is to satisfy the extremely high dynamic range requirements of the simulation box, resolving the Lyman alpha forest while probing the large cosmological scales. In this study, we adopt an efficient approach to model Lyman-$\alpha$ opacity fluctuations in coarse simulation volume, utilizing the semi-numerical reionization model SCRIPT (including inhomogeneous recombination and radiative feedback) integrated with a realistic photoionization background fluctuation generating model. Our model crucially incorporates ionization and temperature fluctuations, which are consistent with the reionization model. After calibrating our method with respect to high-resolution full hydrodynamic simulation, Nyx, we compare the models with available observational data at the redshift range, $z=5.0-6.1$. With a fiducial reionization model (reionization end at $z=5.8$), we demonstrate that the observed scatter in the effective optical depth can be matched reasonably well by tuning the free parameters of our model i.e. the effective ionizing photon mean free path and mean photoionization rate. We further pursue an MCMC-based parameter space exploration utilizing the available data to put constraints on the above free parameters. Our estimation prefers a slightly higher photoionization rate and slightly lower mean free path than the previous studies which is also a consequence of temperature fluctuations. This study holds significant promise for efficiently extracting important physical information about the Epoch of Reionization, utilizing the wealth of available and upcoming observational data.

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