A galaxy-free phenomenological model for the 21-cm power spectrum during reionization

TL;DR

This paper introduces a simple, phenomenological model for the 21-cm power spectrum during reionization that directly relates to IGM properties, bypassing complex simulations and aiding interpretation of observational data.

Contribution

It presents a novel, computationally efficient model that infers IGM properties from 21-cm data without relying on detailed galaxy modeling.

Findings

Model accurately recovers IGM properties from mock data.

Simplified assumptions still yield good agreement with complex simulations.

Approach is complementary to existing semi-numerical models.

Abstract

Upper limits from the current generation of interferometers targeting the 21-cm signal from high redshifts have recently begun to rule out physically realistic, though still extreme, models of the Epoch of Reionization (EoR). While inferring the detailed properties of the first galaxies is one of the most important motivations for measuring the high-$z$ 21-cm signal, they can also provide useful constraints on the properties of the intergalactic medium (IGM). Motivated by this, we build a simple, phenomenological model for the 21-cm power spectrum that works directly in terms of IGM properties, which bypasses the computationally expensive 3-D semi-numerical modeling generally employed in inference pipelines and avoids explicit assumptions about galaxy properties. The key simplifying assumptions are that (i) the ionization field is binary, and composed of spherical bubbles with an abundance described well by a parametric bubble size distribution, and (ii) that the spin temperature of the ``bulk'' IGM outside bubbles is uniform. Despite the simplicity of the model, the mean ionized fraction and spin temperature of the IGM recovered from mock 21-cm power spectra generated with \textsc{21cmfast} are generally in good agreement with the true input values. This suggests that it is possible to obtain comparable constraints on the IGM using models with very different assumptions, parameters, and priors. Our approach will thus be complementary to semi-numerical models as upper limits continue to improve in the coming years.