Optimal identification of HII regions during reionization in 21-cm observations

TL;DR

This paper introduces a superpixels image processing method to improve the identification of ionized regions in noisy 21-cm observations during reionization, enhancing analysis of the Epoch of Reionization.

Contribution

The paper presents a novel superpixels-based approach for identifying ionized regions in 21-cm data, outperforming existing threshold-based methods, especially in noisy conditions.

Findings

Superpixels method yields more accurate ionized region identification.

Superpixels approach outperforms threshold and K-Means methods in noisy data.

Potential applications include deriving ionization history and source properties.

Abstract

The ability of the future low frequency component of the Square Kilometre Array radio telescope (SKA-Low) to produce tomographic images of the redshifted 21-cm signal will enable direct studies of the evolution of the sizes and shapes of ionized regions during the Epoch of Reionization. However, a reliable identification of ionized regions in noisy interferometric data is not trivial. Here, we introduce an image processing method known as superpixels for this purpose. We compare this method with two other previously proposed ones, one relying on a chosen threshold and the other employing automatic threshold determination using the K-Means algorithm. We use a correlation test and compare power spectra and bubble size distributions to show that the superpixels method provides a better identification of ionized regions, especially in the case of noisy data. We also describe some possible additional applications of the superpixel method, namely the derivation of the ionization history and constraints on the source properties in specific regions.