Lost Horizon: Quantifying the Effect of Local Topography on Global 21-cm
  Cosmology Data Analysis
  We present an investigation of the horizon and its effect on global 21-cm
observations and analysis. We find that the horizon cannot be ignored when
modeling low frequency observations. Even if the sky and antenna beam are known
exactly, forward models cannot fully describe the beam-weighted foreground
component without accurate knowledge of the horizon. When fitting data to
extract the 21-cm signal, a single time-averaged spectrum or independent
multi-spectrum fits may be able to compensate for the bias imposed by the
horizon. However, these types of fits lack constraining power on the 21-cm
signal, leading to large uncertainties on the signal extraction, in some cases
larger in magnitude than the 21-cm signal itself. A significant decrease in
signal uncertainty can be achieved by performing multi-spectrum fits in which
the spectra are modeled simultaneously with common parameters. The cost of this
greatly increased constraining power, however, is that the time dependence of
the horizon's effect, which is more complex than its spectral dependence, must
be precisely modeled to achieve a good fit. To aid in modeling the horizon, we
present an algorithm and Python package for calculating the horizon profile
from a given observation site using elevation data. We also address several
practical concerns such as pixelization error, uncertainty in the horizon
profile, and foreground obstructions such as surrounding buildings and
vegetation. We demonstrate that our training set-based analysis pipeline can
account for all of these factors to model the horizon well enough to precisely
extract the 21-cm signal from simulated observations.
