Constraining the reionization history using deep learning from 21-cm tomography with the Square Kilometre Array

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dc.contributor.authorMangena, Tumelo
dc.contributor.authorHassan, Sultan
dc.contributor.authorSantos, Mario G.
dc.date.accessioned2021-02-11T07:32:37Z
dc.date.available2021-02-11T07:32:37Z
dc.date.issued2020
dc.description.abstractUpcoming 21-cm surveys with the SKA1-LOW telescope will enable imaging of the neutral hydrogen distribution on cosmological scales in the early Universe. These surveys are expected to generate huge imaging data sets that will encode more information than the power spectrum. This provides an alternative unique way to constrain the reionization history, which might break the degeneracy in the power spectral analysis. Using convolutional neural networks, we create a fast estimator of the neutral fraction from the 21-cm maps that are produced by our large seminumerical simulation. Our estimator is able to efficiently recover the neutral fraction (⁠xHI⁠) at several redshifts with a high accuracy of 99 per cent as quantified by the coefficient of determination R2.en_US
dc.identifier.citationMangena, T. et al. (2020). Constraining the reionization history using deep learning from 21-cm tomography with the Square Kilometre Array. Monthly Notices of the Royal Astronomical Society, 494(1), 600–606en_US
dc.identifier.issn1365-2966
dc.identifier.urihttps://doi.org/10.1093/mnras/staa750
dc.identifier.urihttp://hdl.handle.net/10566/5902
dc.language.isoenen_US
dc.publisherOxford University Pressen_US
dc.subjectCosmologyen_US
dc.subjectDark agesen_US
dc.subjectIntergalactic mediumen_US
dc.subjectISM:HII regionsen_US
dc.subjectReionizationen_US
dc.subjectFirst starsen_US
dc.titleConstraining the reionization history using deep learning from 21-cm tomography with the Square Kilometre Arrayen_US
dc.typeArticleen_US

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