Comparing Foreground Removal Techniques for Recovery of the LOFAR-EoR 21cm Power Spectrum

TL;DR

This study compares foreground removal techniques for LOFAR data aiming to detect the 21cm signal from the Epoch of Reionization, finding GPR to be the most robust and effective method among those tested.

Contribution

The paper evaluates and compares the performance of FastICA, GMCA, and GPR foreground removal techniques on LOFAR data, highlighting GPR's robustness and suitability for 21cm cosmology.

Findings

GMCA and FastICA reproduce the latest 2σ upper limit on 21cm signal.

FastICA and GMCA deviate from noise limits at k > 0.1 h cMpc^{-1}.

GPR remains the most effective and robust foreground removal method.

Abstract

We compare various foreground removal techniques that are being utilised to remove bright foregrounds in various experiments aiming to detect the redshifted 21cm signal of neutral hydrogen from the Epoch of Reionization. In this work, we test the performance of removal techniques (FastICA, GMCA, and GPR) on 10 nights of LOFAR data and investigate the possibility of recovering the latest upper limit on the 21cm signal. Interestingly, we find that GMCA and FastICA reproduce the most recent 2$\sigma$ upper limit of $\Delta^2_{21} <$ (73)$^2$ mK$^2$ at $k=0.075~ h \mathrm{cMpc}^{-1}$, which resulted from the application of GPR. We also find that FastICA and GMCA begin to deviate from the noise-limit at \textit{k}-scales larger than $\sim 0.1 ~h \mathrm{cMpc}^{-1}$. We then replicate the data via simulations to see the source of FastICA and GMCA's limitations, by testing them against various instrumental effects. We find that no single instrumental effect, such as primary beam effects or mode-mixing, can explain the poorer recovery by FastICA and GMCA at larger \textit{k}-scales. We then test scale-independence of FastICA and GMCA, and find that lower \textit{k}-scales can be modelled by a smaller number of independent components. For larger scales ($k \gtrsim 0.1~h \mathrm{cMpc}^{-1}$), more independent components are needed to fit the foregrounds. We conclude that, the current usage of GPR by the LOFAR collaboration is the appropriate removal technique. It is both robust and less prone to overfitting, with future improvements to GPR's fitting optimisation to yield deeper limits.