Foreground Removal using FastICA: A Showcase of LOFAR-EoR

TL;DR

This paper presents a new FastICA-based method for removing foregrounds from simulated LOFAR EoR data, effectively recovering the 21-cm signal with minimal error and no prior assumptions.

Contribution

A novel implementation of FastICA for foreground removal in 21-cm cosmology, demonstrating high accuracy and robustness without relying on prior foreground models.

Findings

Foregrounds removed with 0.5% average fitting error

Power spectra successfully recovered across frequencies

21-cm variance recovered on large scales despite noise leakage

Abstract

We introduce a new implementation of the FastICA algorithm on simulated LOFAR EoR data with the aim of accurately removing the foregrounds and extracting the 21-cm reionization signal. We find that the method successfully removes the foregrounds with an average fitting error of 0.5 per cent and that the 2D and 3D power spectra are recovered across the frequency range. We find that for scales above several PSF scales the 21-cm variance is successfully recovered though there is evidence of noise leakage into the reconstructed foreground components. We find that this blind independent component analysis technique provides encouraging results without the danger of prior foreground assumptions.