Statistics of the epoch of reionization (EoR) 21-cm signal -- II. The evolution of the power spectrum error-covariance

TL;DR

This paper investigates how the non-Gaussian nature of the EoR 21-cm signal affects the error-covariance of its power spectrum, revealing significant deviations from Gaussian assumptions especially at later reionization stages.

Contribution

It provides a detailed analysis of the evolving error-covariance of the 21-cm power spectrum during reionization using simulations, highlighting the importance of non-Gaussian effects.

Findings

Error variance exceeds Gaussian predictions at small scales early in reionization.

Errors in different k bins are highly correlated, especially at later stages.

Gaussian assumptions underestimate the true errors, necessitating non-Gaussian modeling.

Abstract

The EoR 21-cm signal is expected to become highly non-Gaussian as reionization progresses. This severely affects the error-covariance of the EoR 21-cm power spectrum which is important for predicting the prospects of a detection with ongoing and future experiments. Most earlier works have assumed that the EoR 21-cm signal is a Gaussian random field where (1) the error variance depends only on the power spectrum and the number of Fourier modes in the particular $k$ bin, and (2) the errors in the different $k$ bins are uncorrelated. Here we use an ensemble of simulated 21-cm maps to analyze the error-covariance at various stages of reionization. We find that even at the very early stages of reionization ($\bar{x}_{\rm HI} \sim 0.9 $) the error variance significantly exceeds the Gaussian predictions at small length-scales ($k > 0.5 \,{\rm Mpc}^{-1}$) while they are consistent at larger scales. The errors in most $k$ bins (both large and small scales), are however found to be correlated. Considering the later stages ($\bar{x}_{\rm HI} = 0.15$), the error variance shows an excess in all $k$ bins within $k \ge 0.1 \, {\rm Mpc}^{-1}$, and it is around $200$ times larger than the Gaussian prediction at $k \sim 1 \, {\rm Mpc}^{-1}$. The errors in the different $k$ bins are all also highly correlated, barring the two smallest $k$ bins which are anti-correlated with the other bins. Our results imply that the predictions for different 21-cm experiments based on the Gaussian assumption underestimate the errors, and it is necessary to incorporate the non-Gaussianity for more realistic predictions.