Machine-learning recovery of foreground wedge-removed 21-cm light cones for high-$z$ galaxy mapping

Understand

Upcoming experiments will map the spatial distribution of the 21-cm signal over three-dimensional volumes of space during the Epoch of Reionization (EoR).

• Several methods have been proposed to mitigate the issue of astrophysical foreground contamination in tomographic images of the 21-cm signal, one of which involves the excision of a wedge-shaped region in cylindrical Fourier space.
• While this removes the $k$-modes most readily contaminated by foregrounds, the concurrent removal of cosmological information located within the wedge considerably distorts the structure of 21-cm images.
• In this study, we build upon a U-Net based deep learning algorithm to reconstruct foreground wedge-removed maps of the 21-cm signal, newly incorporating light-cone effects.