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

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 type of background radiation 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 predicting 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 predict 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 valuable tools for cross-confirmation of the 21 cm signal.