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arXiv · 2505.06350

Mapping neutral islands during end stages of reionization with photometric intergalactic medium tomography

Sambit K. Giri·Koki Kakiichi·Michele Bianco·P. Daniel Meerburg
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Abstract

During the epoch of reionization (EoR), the first generation of luminous sources in our Universe emitted ionizing photons that almost completely ionized the gas in the intergalactic medium (IGM). The growth of ionized bubbles and the persistence of neutral islands within the IGM hold vital clues to understanding the morphology and timeline of cosmic reionization. We explore the potential of photometric IGM tomography using deep narrow-band (NB) imaging to observe the Lyman-$α$ forest transmission in background galaxies with the Subaru/Hyper-Suprime Cam (HSC). Based on our simulations, we find that the currently available NB filter is suitable for mapping the IGM at $z\simeq 5.7$, corresponding to the late stages of reionization. Our findings indicate that over $\sim$500 background galaxies are needed to accurately reconstruct the IGM at scales greater than 200 Mpc, achieving more than a 40 per cent correlation with the true distribution. This technique can help detect final remaining neutral islands that span more than 20 Mpc lengths. Using the superpixel method built to identify physical patterns in noisy image data, we find that the neutral island size distribution can be recovered with an accuracy of $\sim$0.3 dex. Furthermore, we demonstrate that these reconstructed maps are correlated with the galaxy distribution and anti-correlated with the cosmological 21-cm signal from neutral hydrogen in the IGM. Lastly, we find that these reconstructed maps are anti-correlated with the patchy optical depth to the cosmic microwave background. As such, multiple measurements can be employed for confirmed detection of neutral islands during the end stages.

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Sambit K. Giri, Koki Kakiichi, Michele Bianco, P. Daniel Meerburg. 2025-11-25. Mapping neutral islands during end stages of reionization with photometric intergalactic medium tomography. https://doi.org/10.1093/mnras%2Fstaf1947

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