Reionization Models Classifier using 21cm Map Deep Learning

TL;DR

This paper introduces a deep learning classifier using CNNs to distinguish between galaxy- and AGN-dominated reionization models from 21cm maps, promising enhanced insights into cosmic reionization sources from upcoming observations.

Contribution

The study develops a CNN-based classifier capable of differentiating reionization models using 21cm maps, resilient to observational noise, advancing analysis methods for future large-scale 21cm data.

Findings

Successfully discriminates between galaxy- and AGN-dominated models

Performs well even with simulated observational noise

Applicable to upcoming 21cm experiments like HERA and SKA

Abstract

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.