Foreground modelling via Gaussian process regression: an application to
  HERA data
  The key challenge in the observation of the redshifted 21-cm signal from
cosmic reionization is its separation from the much brighter foreground
emission. Such separation relies on the different spectral properties of the
two components, although, in real life, the foreground intrinsic spectrum is
often corrupted by the instrumental response, inducing systematic effects that
can further jeopardize the measurement of the 21-cm signal. In this paper, we
use Gaussian Process Regression to model both foreground emission and
instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of
Reionization Array. We find that a simple co-variance model with three
components matches the data well, giving a residual power spectrum with white
noise properties. These consist of an "intrinsic" and instrumentally corrupted
component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating
the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h
cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$
MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should
be distinguishable from the 21-cm EoR signal whose typical coherence-scales is
$\sim 0.8$ MHz.
