Classification of Radio Backgrounds at Cosmic Dawn and 21 cm Signal Confirmation Using Neural Networks

TL;DR

This paper uses neural networks to classify radio backgrounds during cosmic dawn and to reconstruct 21 cm signals from power spectrum data, aiding in the confirmation of cosmological observations.

Contribution

It introduces neural network methods for classifying radio backgrounds and reconstructing 21 cm signals, improving analysis of cosmic dawn data.

Findings

ANN predicts background type with 96% accuracy on clean data

ANN reconstructs global signals from power spectra with high consistency

Results remain useful despite observational noise

Abstract

Several ongoing and upcoming radio telescopes aim to detect either the global 21 cm signal or the 21 cm power spectrum. The extragalactic radio background, as detected by ARCADE-2 and LWA-1, suggests a strong radio background from cosmic dawn, which can significantly alter the cosmological 21 cm signal, enhancing both the global signal amplitude and the 21 cm power spectrum. In this paper, we employ an artificial neural network (ANN) to check if there is a radio excess over the cosmic microwave background in mock data, and if present, we classify its type into one of two categories, a background from high-redshift radio galaxies or a uniform exotic background from the early Universe. Based on clean data (without observational noise), the ANN can predict the background radiation type with $96\%$ accuracy for the power spectrum and $90\%$ for the global signal. Although observational noise reduces the accuracy, the results remain quite useful. We also apply ANNs to map the relation between the 21 cm power spectrum and the global signal. By reconstructing the global signal using the 21 cm power spectrum, an ANN can estimate the global signal range consistent with an observed power spectrum from SKA-like experiments. Conversely, we show that an ANN can reconstruct the 21 cm power spectrum over a wide range of redshifts and wavenumbers given the global signal over the same redshifts. Such trained networks can potentially serve as a valuable tool for cross-confirmation of the 21 cm signal.