The Scale of the Problem : Recovering Images of Reionization with GMCA

TL;DR

This paper demonstrates that GMCA, a non-parametric technique, effectively removes foregrounds from simulated Epoch of Reionization data, accurately recovering the 21-cm power spectra and maps, especially when combined with wavelet decomposition.

Contribution

The study introduces the application of GMCA to EoR data and shows wavelet decomposition's superiority over smoothing for denoising 21-cm maps.

Findings

GMCA accurately recovers 21-cm power spectra across frequencies and scales.

Wavelet decomposition outperforms smoothing in denoising 21-cm maps.

High correlation coefficients indicate effective foreground removal and signal recovery.

Abstract

The accurate and precise removal of 21-cm foregrounds from Epoch of Reionization redshifted 21-cm emission data is essential if we are to gain insight into an unexplored cosmological era. We apply a non-parametric technique, Generalized Morphological Component Analysis or GMCA, to simulated LOFAR-EoR data and show that it has the ability to clean the foregrounds with high accuracy. We recover the 21-cm 1D, 2D and 3D power spectra with high accuracy across an impressive range of frequencies and scales. We show that GMCA preserves the 21-cm phase information, especially when the smallest spatial scale data is discarded. While it has been shown that LOFAR-EoR image recovery is theoretically possible using image smoothing, we add that wavelet decomposition is an efficient way of recovering 21-cm signal maps to the same or greater order of accuracy with more flexibility. By comparing the GMCA output residual maps (equal to the noise, 21-cm signal and any foreground fitting errors) with the 21-cm maps at one frequency and discarding the smaller wavelet scale information, we find a correlation coefficient of 0.689, compared to 0.588 for the equivalently smoothed image. Considering only the central 50% of the maps, these coefficients improve to 0.905 and 0.605 respectively and we conclude that wavelet decomposition is a significantly more powerful method to denoise reconstructed 21-cm maps than smoothing.