التفاصيل البيبلوغرافية
| العنوان: |
Machine-learning recovery of foreground wedge-removed 21-cm light cones for high-z galaxy mapping. |
| المؤلفون: |
Kennedy, Jacob, Carr, Jonathan Colaço, Gagnon-Hartman, Samuel, Liu, Adrian, Mirocha, Jordan, Cui, Yue |
| المصدر: |
Monthly Notices of the Royal Astronomical Society; Apr2024, Vol. 529 Issue 4, p3684-3698, 15p |
| مصطلحات موضوعية: |
LIGHT cones, MACHINE learning, INTERSTELLAR medium, GALACTIC redshift, TOMOGRAPHY, ASTRONOMICAL perturbation, COSMIC background radiation |
| مستخلص: |
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. Adopting the Square Kilometre Array as our fiducial instrument, we highlight that our U-Net recovery framework retains a reasonable level of reliability even in the face of instrumental limitations and noise. We subsequently evaluate the efficacy of recovered maps in guiding high-redshift galaxy searches and providing context to existing galaxy catalogues. This will allow for studies of how the high-redshift galaxy luminosity function varies across environments, and ultimately refine our understanding of the connection between the ionization state of the intergalactic medium and galaxies during the EoR. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
