Year: 2025
Type: article
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 .
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,000
Awards: G2366492356, NSF-BSF: Dark-Matter Interactions at Cosmic Dawn, THEORETICAL HIGH ENERGY PHYSICS, AI Institute: AI Research Institute for Fundamental Interactions, MRI: Acquisition of an Advanced Computing Instrument to Integrate Data-Driven Research and Data intensive computing at Johns Hopkins University, G5968496389, String Theory and Quantum Field Theory: From the Planck Scale to the Hubble Scale, THEORETICAL HIGH ENERGY PHYSICS, G6794394390, Decoding the First Galaxies via 21-cm Fluctuations, G97843138
OpenAlex ID: W4407090583