Reionization Parameter Inference from 3D Minkowski Functionals of the 21 cm Signals

TL;DR

This paper explores the use of Minkowski Functionals to extract non-Gaussian information from 21 cm signals during reionization, demonstrating their potential to improve parameter constraints with future radio observations.

Contribution

It introduces a method to use Minkowski Functionals for constraining reionization parameters from 21 cm data, including realistic observational effects and joint analysis with power spectra.

Findings

Minkowski Functionals can distinguish different reionization models with SKA-Low.

MF measurements with HERA-like arrays are less accurate.

Joint MF and PS analysis improves parameter constraints by up to 30%.

Abstract

The Minkowski Functionals (MFs), a set of topological summary statistics, have emerged as a powerful tool for extracting non-Gaussian information. We investigate the prospect of constraining the reionization parameters using the MFs of the 21 cm brightness temperature field from the epoch of reionization (EoR). Realistic effects, including thermal noise, synthesized beam, and foreground avoidance, are applied to the mock observations from the radio interferometric array experiments such as the Hydrogen Epoch of Reionization Array (HERA) and the Square Kilometre Array (SKA). We demonstrate that the MFs of the 21 cm signal measured with SKA-Low can be used to distinguish different reionization models, whereas the MF measurement with a HERA-like array cannot be made accurately enough. We further forecast the accuracies with which the MF measurements can place constraints on reionization parameters, using the standard MCMC analysis for parameter inference based on forward modeling. We find that for SKA-Low observation, MFs provide unbiased estimations of the reionization parameters with accuracies comparable to the power spectrum (PS) analysis. Furthermore, joint constraints using both MFs and PS can improve the constraint accuracies by up to $30\%$ compared to those with the PS alone. Nevertheless, the constraint accuracies can be degraded if the EoR window is shrunk with strong foreground avoidance. Our analysis demonstrates the promise of MFs as a set of summary statistics that extract complementary information from the 21 cm EoR field to the two-point statistics, which suggests a strong motivation for incorporating the MFs into the data analysis of future 21 cm observations.