A generative modeling approach to reconstructing 21 cm tomographic data

Journal Article · 13 February 2025 · Machine Learning: Science and Technology

DOI: https://doi.org/10.1088/2632-2153/adb19c · OSTI ID:2519046

Sabti, Nashwan ; Purandhar Reddy Sudha, Ram ; Muñoz, Julian B. ; Mishra-Sharma, Siddharth ; Youn, Taewook

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. Code, pre-trained models, and scripts for making plots in this paper can be found here .

• Sponsoring Organization: USDOE
• Grant/Contract Number: SC0012567
• OSTI ID: 2519046
• Journal Information: Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 1 Vol. 6; ISSN 2632-2153
• Publisher: IOP PublishingCopyright Statement
• Country of Publication: United Kingdom
• Language: English