Gaussian Process Regression for foreground removal in HI intensity mapping experiments

TL;DR

This paper demonstrates the effectiveness of Gaussian Process Regression (GPR) for foreground removal in low-redshift HI intensity mapping, showing it can outperform PCA in certain scenarios, especially for radial power spectrum recovery.

Contribution

First application of GPR for foreground removal in HI intensity mapping with an open-source Python toolkit, comparing its performance to PCA using realistic simulations.

Findings

GPR can effectively remove foregrounds in HI intensity mapping.

GPR outperforms PCA in recovering the HI radial power spectrum.

GPR's performance varies with data bandwidth and missing channels.

Abstract

We apply for the first time Gaussian Process Regression (GPR) as a foreground removal technique in the context of single-dish, low redshift HI intensity mapping, and present an open-source Python toolkit for doing so. We use MeerKAT and SKA1-MID-like simulations of 21cm foregrounds (including polarisation leakage), HI cosmological signal and instrumental noise. We find that it is possible to use GPR as a foreground removal technique in this context, and that it is better suited in some cases to recover the HI power spectrum than Principal Component Analysis (PCA), especially on small scales. GPR is especially good at recovering the radial power spectrum, outperforming PCA when considering the full bandwidth of our data. Both methods are worse at recovering the transverse power spectrum, since they rely on frequency-only covariance information. When halving our data along frequency, we find that GPR performs better in the low frequency range, where foregrounds are brighter. It performs worse than PCA when frequency channels are missing, to emulate RFI flagging. We conclude that GPR is an excellent foreground removal option for the case of single-dish, low redshift HI intensity mapping in the absence of missing frequency channels. Our Python toolkit gpr4im and the data used in this analysis are publicly available on GitHub.