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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: sultanier@gmail.com 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

Information

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 

References

Becker, G. D. & Bolton, J. S., 2013, MNRAS, 436, 1023Google Scholar
Becker, G. D., et al. 2015, MNRAS, 447, 3402Google Scholar
Bond, J. R., Cole, S., Efstathiou, G., & Kaiser, N., 1991, APJ, 379, 440Google Scholar
Davé, R., et al. 2013, MNRAS, 434, 2645Google Scholar
Fan, X., Carilli, C. L., & Keating, B., 2006, Annual Review of Astronomy and Astrophysics, 44, 415Google Scholar
Ferrarese, L., 2002, APJ, 578, 90Google Scholar
Finlator, K., et al. 2015, MNRAS, 447, 2526Google Scholar
Giallongo, E., et al. 2015, A&A, 578, A83Google Scholar
Hassan, S., et al. 2016, MNRAS, 457, 1550Google Scholar
Hassan, S., et al. 2017, MNRAS, 468, 122CrossRefGoogle Scholar
Hassan, S., et al. 2018, MNRAS, 473, 227Google Scholar
Hopkins, P. F., Richards, G. T., & Hernquist, L., 2007, APJ, 654, 731Google Scholar
Planck intermediate results. XLVII, Adam, R., Aghanim, N., et al. 2016, arXiv:1605.03507Google Scholar
Pober, J. C., Liu, A. et al. 2014, APJ, 782, 66.Google Scholar
Press, W. H. & Schechter, P., 1974, APJ, 187, 425.Google Scholar
Santos, M. G., et al. 2010, MNRAS, 406, 2421Google Scholar
Shapiro, P. R. & Giroux, M. L., 1987, APJ, 321, 107Google Scholar
Tremaine, S., et al. 2002, APJ, 574, 740Google Scholar
Worseck, G., et al. 2016, APJ, 825, 144Google Scholar