A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

Julian B. Mu\~noz; Nashwan Sabti; Ram Reddy; Siddharth Mishra-Sharma; Taewook Youn

A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

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arxiv 2407.21097 v1 pith:DRP2B3BN submitted 2024-07-30 astro-ph.CO astro-ph.GA

A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

Nashwan Sabti , Ram Reddy , Julian B. Mu\~noz , Siddharth Mishra-Sharma , Taewook Youn This is my paper

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Analyses of the cosmic 21-cm signal are hampered by astrophysical foregrounds that are far stronger than the signal itself. These foregrounds, typically confined to a wedge-shaped region in Fourier space, often necessitate the removal of a vast majority of modes, thereby degrading the quality of the data anisotropically. To address this challenge, we introduce a novel deep generative model based on stochastic interpolants to reconstruct the 21-cm data lost to wedge filtering. Our method leverages the non-Gaussian nature of the 21-cm signal to effectively map wedge-filtered 3D lightcones to samples from the conditional distribution of wedge-recovered lightcones. We demonstrate how our method is able to restore spatial information effectively, considering both varying cosmological initial conditions and astrophysics. Furthermore, we discuss a number of future avenues where this approach could be applied in analyses of the 21-cm signal, potentially offering new opportunities to improve our understanding of the Universe during the epochs of cosmic dawn and reionization.

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Cited by 1 Pith paper

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Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions
astro-ph.CO 2026-06 unverdicted novelty 6.0

Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.