Sensitivity of the Hydrogen Epoch of Reionization Array and its Build-out Stages to One-point Statistics from Redshifted 21 cm Observations

TL;DR

This study assesses HERA's ability to detect one-point statistics of 21 cm signals from the Epoch of Reionization, showing high sensitivity for variance and potential for skewness and kurtosis detection, with implications for understanding reionization structures.

Contribution

It provides a comprehensive sensitivity analysis of HERA and its build-out stages for one-point statistics, incorporating realistic models and measurement schemes, highlighting detection capabilities and limitations.

Findings

HERA can detect variance across all build-out stages.

Skewness and kurtosis are detectable for HERA128 and larger.

Sample variance limits variance measurement, while thermal noise affects skewness and kurtosis.

Abstract

We present a baseline sensitivity analysis of the Hydrogen Epoch of Reionization Array (HERA) and its build-out stages to one-point statistics (variance, skewness, and kurtosis) of redshifted 21 cm intensity fluctuation from the Epoch of Reionization (EoR) based on realistic mock observations. By developing a full-sky 21 cm lightcone model, taking into account the proper field of view and frequency bandwidth, utilising a realistic measurement scheme, and assuming perfect foreground removal, we show that HERA will be able to recover statistics of the sky model with high sensitivity by averaging over measurements from multiple fields. All build-out stages will be able to detect variance, while skewness and kurtosis should be detectable for HERA128 and larger. We identify sample variance as the limiting constraint of the variance measurement while skewness and kurtosis measurements will be primarily limited by thermal noise. The sensitivity can be improved by performing frequency binning and windowing. In addition, we find that strong sample variance fluctuation in the kurtosis measured from an individual field of observation indicates the present of outlying cold or hot regions in the underlying fluctuations, a feature that can potentially be used as an EoR bubble indicator. This manuscript is altered from the originally published paper to reflect corrections in the erratum.