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Deep Learning 21cm Lightcones in 3D
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Deep Learning 21cm Lightcones in 3D
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Interferometric measurements of the 21cm signal are a prime example of the data-driven era in astrophysics we are entering with current and upcoming experiments. We showcase the use of deep networks that are tailored for the structure of 3D tomographic 21cm light-cones to firstly detect and characterise HI sources and to secondly directly infer global astrophysical and cosmological model parameters. We compare different architectures and highlight how 3D CNN architectures that mirror the data structure are the best-performing model.
Forward citations
Cited by 9 Pith papers
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A review chapter summarizing theoretical 21-cm signatures from Cosmic Dawn and Reionization and their detectability with SKA-Low.
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Overview of SKA-Low 21cm experiments for high-redshift cosmology, covering power spectra, tomography, 21cm forest, cross-correlations, and key telescope features.
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An overview summarizing SKA-Low 21cm experiments for power spectrum, tomography, 21-cm forest, and cross-correlations, plus critical telescope features, building on the 2015 SKA Science Book.
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