Identifying Reionization Sources from 21cm Maps using Convolutional Neural Networks

Abstract

Active Galactic Nuclei (AGN) and star-forming galaxies are leading candidates for being the luminous sources that reionized our Universe. Next-generation 21cm surveys are promising to break degeneracies between a broad range of reionization models, hence revealing the nature of the source population. While many current efforts are focused on a measurement of the 21cm power spectrum, some surveys will also image the 21cm field during reionization. This provides further information with which to determine the nature of reionizing sources. We create a Convolutional Neural Network (CNN) that is efficiently able to distinguish between 21cm maps that are produced by AGN versus galaxies scenarios with an accuracy of 92-100%, depending on redshift and neutral fraction range. An exception to this is when our Universe is highly ionized, since the source models give near-identical 21cm maps in that case. When adding thermal noise from typical 21cm experiments, the classification accuracy depends strongly on the effectiveness of foreground removal. Our results show that if foregrounds can be removed reasonably well, SKA, HERA and LOFAR should be able to discriminate between source models with greater accuracy at a fixed redshift. Only future SKA 21cm surveys are promising to break the degeneracies in the power spectral analysis.

16 pages, 10 figures, MNRAS accepted

References in corpus (11)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Cosmology at Low Frequencies: The 21 cm Transition and the High-Redshift Universe
• An Observational Determination of the Bolometric Quasar Luminosity Function
• Evidence of patchy hydrogen reionization from an extreme Ly$\alpha$ trough below redshift six
• Non-Gaussian information from weak lensing data via deep learning
• Deep learning from 21-cm tomography of the Cosmic Dawn and Reionization
• Optimal identification of HII regions during reionization in 21-cm observations
• Imaging the redshifted 21-cm pattern around the first sources during the cosmic dawn using the SKA
• Classifying the Large Scale Structure of the Universe with Deep Neural Networks
• The Reionization of Carbon
• Epoch of Reionisation 21cm Forecasting From MCMC-Constrained Semi-Numerical Models

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