Probing ionized bubbles around luminous sources during reionization with SKA 21-cm observations

TL;DR

This paper presents a new efficient method for detecting and characterizing ionized bubbles during the Epoch of Reionization using SKA 21-cm observations, including a novel estimator, foreground subtraction, and Bayesian analysis.

Contribution

It introduces a computationally efficient estimator for ionized bubble detection, a non-parametric foreground removal technique, and a Bayesian framework for parameter recovery, advancing EoR studies with SKA.

Findings

Ionized bubbles at z~7-8 can be detected with SNR > 10 in ~100 hours.

The scaling relation predicts SNR based on bubble size, redshift, and observation time.

Bayesian analysis accurately recovers bubble properties from residual visibilities.

Abstract

Detecting and characterizing individual ionized bubbles during the Epoch of Reionization (EoR) using the redshifted HI 21-cm signal provides a direct probe of the early ionizing sources and the intergalactic medium. We develop and validate a computationally efficient estimator that operates on gridded visibilities to detect ionized bubbles. This serves as an accurate alternative to the more computationally demanding bare estimator that uses all baselines and frequency channels. Further, we employ a non-parametric foreground-subtraction method based on Gaussian process regression, which minimizes loss of the HI 21-cm signal and yields improved signal-to-noise ratios. Our analysis indicates that ionized bubbles at redshifts $z \sim 7 - 8$ can be detected with SNR $\gtrsim 10$ using $\sim 100$ hours of SKA1-Low AA$^*$ and AA4 observations. We further derive a scaling relation that connects the SNR to the bubble radius, redshift, total observing time, and the mean neutral hydrogen fraction of the surrounding IGM. This helps to quickly predict the observational outcome for any planned observations and is, therefore, useful for devising observational strategies. Finally, we apply a Bayesian likelihood framework with Markov Chain Monte Carlo sampling to the residual visibilities to recover ionized bubble properties, including radius, position, and the mean neutral fraction. The resulting posterior distributions demonstrate accurate recovery of the bubble parameters. This confirms the feasibility of robustly characterizing individual ionized regions with the SKA1-Low.