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

TL;DR

This work tackles the direct detection and characterization of individual ionized bubbles during the Epoch of Reionization through redshifted 21-cm observations with SKA1-Low. It combines a fast gridded matched-filter estimator with Gaussian Process Regression foreground subtraction, derives SKA1-Low–specific SNR scaling relations, and applies Bayesian MCMC to recover bubble radius, position, and surrounding neutral fraction from residual visibilities. The results show that ionized bubbles at $z\sim 7{-}8$ can be detected with $\mathrm{SNR} \gtrsim 10$ in ~100 hours for SKA1-Low AA* or AA4 configurations, and that the five-parameter Bayesian framework yields accurate posteriors consistent with input values. Collectively, the method offers a practical, scalable path to imaging and characterizing individual ionized regions during reionization, with concrete guidance for observational planning and strategy.

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.

Figures (12)

• Figure 1: Station layouts of the SKA1-Low configurations: AA2 (left), AA$^{\star}$ (middle), and AA4 (right).
• Figure 2: $uv$-coverage for $8$ hours of observation at $\nu = 175$ MHz for the SKA1-Low configurations AA2 (left), AA$^{\star}$ (middle), and AA4 (right).
• Figure 3: Cumulative distribution function (CDF) of baseline lengths for the SKA1-Low configurations AA2, AA$^{\star}$ and AA4 at $\nu=175$ MHz.
• Figure 4: This shows the comparison of the recovered visibility (red dashed lines) after subtracting the foreground contribution using the GPR with the original visibility consisting of the HI 21-cm signal and noise contributions (blue lines).
• Figure 5: Comparison between the bare estimator(dashed line) and fast estimator(solid line) for the SKA1-Low AA$^{\star}$ configuration at $z=8.3$. The plot shows the signal-to-noise ratio (SNR) as a function of filter size. Both estimators employ GPR-based foreground subtraction method.