Machine Learning to Decipher the Astrophysical Processes at Cosmic Dawn
  The cosmic 21-cm line of hydrogen is expected to be measured in detail by the
next generation of radio telescopes. The enormous dataset from future 21-cm
surveys will revolutionize our understanding of early cosmic times. We present
a machine learning approach that uses emulation in order to uncover the
astrophysics in the epoch of reionization and cosmic dawn. Using a
seven-parameter astrophysical model that covers a very wide range of possible
21-cm signals, over the redshift range $6$ to $30$ and wavenumber range $0.05 \
\rm{Mpc}^{-1}$ to $1 \ \rm{Mpc}^{-1}$ we emulate the 21-cm power spectrum with
a typical accuracy of $10 - 20\%$. As a realistic example, we train an emulator
using the 21-cm power spectrum with an optimistic model for observational noise
as expected for the Square Kilometre Array (SKA). Fitting to mock SKA data
results in a typical measurement accuracy of $5\%$ in the optical depth to the
CMB, $30\%$ in the star-formation efficiency of galactic halos, and a factor of
$3.5$ in the X-ray efficiency of galactic halos; the latter two parameters are
currently uncertain by orders of magnitude. In addition to standard
astrophysical models, we also consider two exotic possibilities of strong
excess radio backgrounds at high redshifts. We use a neural network to identify
the type of radio background present in the 21-cm power spectrum, with an
accuracy of $87\%$ for mock SKA data.
