Lost Horizon: Quantifying the Effect of Local Topography on Global 21-cm Cosmology Data Analysis

TL;DR

This paper investigates the impact of local topography on global 21-cm cosmology data analysis, emphasizing the importance of accurate horizon modeling to improve signal extraction and reduce uncertainties.

Contribution

It introduces an algorithm and Python package for precise horizon profile modeling, accounting for local obstructions and uncertainties, to enhance 21-cm signal analysis.

Findings

Ignoring the horizon leads to significant biases in 21-cm signal modeling.

Multi-spectrum fitting reduces uncertainties compared to single-spectrum methods.

Accurate horizon modeling enables precise 21-cm signal extraction from simulations.

Abstract

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.