Using Artificial Neural Networks to extract the 21-cm Global Signal from
  the EDGES data
  The redshifted 21-cm signal of neutral Hydrogen is a promising probe into the
period of evolution of our Universe when the first stars were formed (Cosmic
Dawn), to the period where the entire Universe changed its state from being
completely neutral to completely ionized (Reionization). The most striking
feature of this line of neutral Hydrogen is that it can be observed across an
entire frequency range as a sky-averaged continuous signature, or its
fluctuations can be measured using an interferometer. However, the 21-cm signal
is very faint and is dominated by a much brighter Galactic and extra-galactic
foregrounds, making it an observational challenge. We have used different
physical models to simulate various realizations of the 21-cm Global signals,
including an excess radio background to match the amplitude of the EDGES 21-cm
signal. First, we have used an artificial neural network (ANN) to extract the
astrophysical parameters from these simulated datasets. Then, mock observations
were generated by adding a physically motivated foreground model and an ANN was
used to extract the astrophysical parameters from such data. The $R^2$ score of
our predictions from the mock-observations is in the range of 0.65-0.89. We
have used this ANN to predict the signal parameters giving the EDGES data as
the input. We find that the reconstructed signal closely mimics the amplitude
of the reported detection. The recovered parameters can be used to infer the
physical state of the gas at high redshifts.
