Reionization Models Classifier using 21cm Map Deep Learning

Sultan Hassan, Adrian Liu, Saul Kohn, James E. Aguirre, Paul La Plante, Adam Lidz

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)47-51
Number of pages5
JournalProceedings of the International Astronomical Union
Issue numberS333
StatePublished - 2017


  • abundances
  • cosmology: early universe
  • evolution
  • formation
  • galaxies: intergalactic medium
  • methods: data analysis
  • quasars: general

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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