FlexKnot and Gaussian Process for 21 cm global signal analysis and foreground separation

TL;DR

This paper introduces two novel methods, a foreground-orthogonal Gaussian Process and a FlexKnot parameterization, for extracting the cosmological 21 cm signal and separating it from foregrounds in global signal experiments, validated on synthetic and real data.

Contribution

It presents new non-parametric and Bayesian approaches for modeling and extracting the 21 cm signal, improving over incomplete existing models.

Findings

Gaussian Process captures foreground-orthogonal signal components effectively

FlexKnot recovers the full shape of synthetic signals accurately

Multi-modal distributions suggest multiple plausible signal shapes for EDGES data

Abstract

The cosmological 21 cm signal is one of the most promising avenues to study the Epoch of Reionization. One class of experiments aiming to detect this signal is global signal experiments measuring the sky-averaged 21 cm brightness temperature as a function of frequency. A crucial step in the interpretation and analysis of such measurements is separating foreground contributions from the remainder of the signal, requiring accurate models for both components. Current models for the signal (non-foreground) component, which may contain cosmological and systematic contributions, are incomplete and unable to capture the full signal. We propose two new methods for extracting this component from the data: Firstly, we employ a foreground-orthogonal Gaussian Process to extract the part of the signal that cannot be explained by the foregrounds. Secondly, we use a FlexKnot parameterization to model the full signal component in a free-form manner, not assuming any particular shape or functional form. This method uses Bayesian model selection to find the simplest signal that can explain the data. We test our methods on both, synthetic data and publicly available EDGES low-band data. We find that the Gaussian Process can clearly capture the foreground-orthogonal signal component of both data sets. The FlexKnot method correctly recovers the full shape of the input signal used in the synthetic data and yields a multi-modal distribution of different signal shapes that can explain the EDGES observations.