Foreground modelling via Gaussian process regression: an application to HERA data

Abstract

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 ∼2 h 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 and 2.4 MHz, respectively (dominating the line-of-sight power spectrum over scales k∥ ≤ 0.2 h cMpc−1) and a baseline-dependent periodic signal with a period of ∼1 MHz (dominating over k∥ ∼ 0.4–0.8 h cMpc−1), which should be distinguishable from the 21-cm Epoch of Reionization signal whose typical coherence scale is ∼0.8 MHz.

SKA-Low Intensity Mapping Pathfinder Updates: Deeper 21 cm Power Spectrum Limits from Improved Analysis Frameworks

“…For example, radio frequency interference (RFI) mitigation software developed for LOFAR [37] is also used on the MWA, and the same ultrafaint RFI mitigation software [58] is used by the MWA and HERA. Additionally, redundant calibration techniques have been successfully applied to both HERA and the MWA [53,87], and statistical foreground modeling software has shown promise on both LOFAR and HERA data [49,145].…”

Imaging the Southern Sky at 159MHz using Spherical Harmonics with the Engineering Development Array 2

“…Much effort has been put in to attempt to constrain the boundaries of the EoR through directly probing the intergalactic medium (IGM), e.g. determining the foregrounds are generally three to five orders of magnitude brighter than the signal we desire to detect (Morales & Wyithe, 2010;Vedantham et al, 2012;Mertens et al, 2018;Trott et al, 2020;Ghosh et al, 2020). In order to solve for this, foreground mitigation methods have to be employed.…”

First Results from HERA Phase I: Upper Limits on the Epoch of Reionization 21 cm Power Spectrum

“…), and pipeline validation (Aguirre et al in prep.). Complementary studies on the data set discussed in this work also include foreground modeling (Ghosh et al 2020), imaging (Carilli et al 2018), power spectrum analysis of the bispectrum phase (Thyagarajan et al 2020), antenna primary beam characterization (Nunhokee et al 2017), and electromagnetic modeling of the front-end signal chain (Fagnoni et al 2021). Here, we give an overview of the full analysis pipeline, discuss the criteria used for data selection, present the power spectrum limits, and describe statistical tests used to characterize the performance of the system and our analysis techniques.…”