Machine Learning Applied to the Reionization History of the Universe in
  the 21 cm Signal
  The Epoch of Reionization (EoR) features a rich interplay between the first
luminous sources and the low-density gas of the intergalactic medium (IGM),
where photons from these sources ionize the IGM. There are currently few
observational constraints on key observables related to the EoR, such as the
midpoint and duration of reionization. Although upcoming observations of the 21
cm power spectrum with next-generation radio interferometers such as the
Hydrogen Epoch of Reionization Array (HERA) and the Square Kilometre Array
(SKA) are expected to provide information about the midpoint of reionization
readily, extracting the duration from the power spectrum alone is a more
difficult proposition. As an alternative method for extracting information
about reionization, we present an application of convolutional neural networks
(CNNs) to images of reionization. These images are two-dimensional in the plane
of the sky, and extracted at a series of redshift values to generate "image
cubes" that are qualitatively similar to those of the HERA and the SKA will
generate in the near future. Additionally, we include the impact that the
bright foreground signal from the the Milky Way imparts on such image cubes
from interferometers, but do not include the noise induced from observations.
We show that we are able to recover the duration of reionization $\Delta$z to
within 5% using CNNs, assuming that the midpoint of reionization is already
relatively well constrained. These results have exciting impacts for estimating
$\tau$, the optical depth to the cosmic microwave background, which can help
constrain other cosmological parameters.
