The impact of inhomogeneous subgrid clumping on cosmic reionization II: modelling stochasticity

TL;DR

This paper develops a stochastic, density-dependent model for small-scale gas clumping in cosmic reionization, improving predictions of reionization timing, morphology, and 21-cm signal fluctuations compared to simpler models.

Contribution

It introduces an empirical stochastic model for sub-grid gas clumping based on high-resolution simulations, enhancing the realism of reionization simulations.

Findings

Stochastic model reproduces mean density-clumping relation and scatter.

Mean clumping model delays reionization despite fewer recombinations.

Stochastic model suppresses 21-cm fluctuations around overlap.

Abstract

Small-scale density fluctuations can significantly affect reionization but are typically modelled quite crudely. Unresolved fluctuations in numerical simulations and analytical calculations are included using a gas clumping factor, typically assumed to be independent of the local environment. In Paper I, we presented an improved, local density-dependent model for the sub-grid gas clumping. Here we extend this using an empirical stochastic model based on the results from high-resolution numerical simulations which fully resolve all relevant fluctuations. Our model reproduces well both the mean density-clumping relation and its scatter. We applied our stochastic model, along with the mean clumping one and the Paper I deterministic model, to create a large-volume realisation of the clumping field, and used these in radiative transfer simulations of cosmic reionization. Our results show that the simplistic mean clumping model delays reionization compared to local density-dependent models, despite producing fewer recombinations overall. This is due to the very different spatial distribution of clumping, resulting in much higher photoionization rates in the latter cases. The mean clumping model produces smaller HII regions throughout most of the reionization, but those percolate faster at late times. It also causes a significant delay in the 21-cm fluctuations peak and yields lower non-Gaussianity and many fewer bright pixels in the PDF distribution. The stochastic density-dependent model shows relatively minor differences from the deterministic one, mostly concentrated around overlap, where it significantly suppresses the 21-cm fluctuations, and at the bright tail of the 21-cm PDFs, where it produces noticeably more bright pixels.