A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data
Abstract
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.
Cite
@article{arxiv.2407.21097,
title = {A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data},
author = {Nashwan Sabti and Ram Reddy and Julian B. Muñoz and Siddharth Mishra-Sharma and Taewook Youn},
journal= {arXiv preprint arXiv:2407.21097},
year = {2024}
}
Comments
12 pages, 5 figures