Kavli Affiliate: George Efstathiou
| First 5 Authors: Sudipta Sikder, Anastasia Fialkov, Rennan Barkana, ,
| Summary:
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 (CMB) 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.
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