IslandFAST: A Semi-numerical Tool for Simulating the Late Epoch of Reionization

Abstract

We present the algorithm and main results of our semi-numerical simulation, islandFAST, which is developed from the 21cmFAST and designed for the late stage of reionization. The islandFAST predicts the evolution and size distribution of the large scale under-dense neutral regions (neutral islands), and we find that the late Epoch of Reionization (EoR) proceeds very fast, showing a characteristic scale of the neutral islands at each redshift. Using islandFAST, we compare the impact of two types of absorption systems, i.e. the large scale under-dense neutral islands versus small scale over-dense absorbers, in regulating the reionization process. The neutral islands dominate the morphology of the ionization field, while the small scale absorbers dominate the mean free path of ionizing photons, and also delay and prolong the reionization process. With our semi-numerical simulation, the evolution of the ionizing background can be derived self-consistently given a model for the small absorbers. The hydrogen ionization rate of the ionizing background is reduced by an order of magnitude in the presence of dense absorbers.

15 pages, 8 figures

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• Improved treatments of the ionizing photon mean free path in semi-numerical simulations of reionization
• The large-scale 21-cm power spectrum from reionization
• Opening Reionization: Quantitative Morphology of the Epoch of Reionization and Its Connection to the Cosmic Density Field
• The HI Bias during the Epoch of Reionization
• Effects of Small-Scale Absorption Systems on Neutral Islands during the Late Epoch of Reionization
• Reionization Parameter Inference from 3D Minkowski Functionals of the 21 cm Signals
• Reionizing islands with inhomogeneous recombinations
• The Neutral Islands during the Late Epoch of Reionization
• A local, topology-independent parameterization of quasar IGM damping wings
• Mapping neutral islands during end stages of reionization with photometric intergalactic medium tomography
• Uncertainty-Aware Neural Networks for Fuzzy Dark Matter Model Selection from \texorpdfstring{$x_{\mathrm{HI}}$}{x_HI} Measurements