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Reionization Models Classifier using 21cm Map Deep Learning

Published online by Cambridge University Press:  08 May 2018

Sultan Hassan ,
Adrian Liu ,
Saul Kohn ,
James E. Aguirre ,
Paul La Plante  and
Adam Lidz
Sultan Hassan
Affiliation:
The Department of Physics and Astronomy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa email: [email protected] Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
Adrian Liu
Affiliation:
Department of Astronomy and Radio Astronomy Laboratory, University of California Berkeley, Berkeley, CA 94720, USA
Saul Kohn
Affiliation:
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
James E. Aguirre
Affiliation:
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
Paul La Plante
Affiliation:
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
Adam Lidz
Affiliation:
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
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Abstract

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Next-generation 21cm observations will enable imaging of reionization on very large scales. These images will contain more astrophysical and cosmological information than the power spectrum, and hence providing an alternative way to constrain the contribution of different reionizing sources populations to cosmic reionization. Using Convolutional Neural Networks, we present a simple network architecture that is sufficient to discriminate between Galaxy-dominated versus AGN-dominated models, even in the presence of simulated noise from different experiments such as the HERA and SKA.

Keywords

methods: data analysisgalaxies: intergalactic mediumabundancesformationevolutionquasars: generalcosmology: early universe
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S333: Peering towards Cosmic Dawn , October 2017 , pp. 47 - 51
Copyright
Copyright © International Astronomical Union 2018 

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