# Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method

## TL;DR

This paper introduces a convolutional denoising autoencoder that effectively separates the faint EoR signal from foreground contamination in simulated SKA images, outperforming traditional methods.

## Contribution

The novel deep-learning-based CDAE method demonstrates superior performance in EoR signal separation, especially under realistic beam effects, compared to traditional techniques.

## Key findings

- CDAE achieves a mean correlation coefficient of 0.929 with the input EoR signal.
- Traditional methods like polynomial fitting and wavelet transform perform poorly under beam effects.
- Deep learning shows great potential for future EoR data analysis.

## Abstract

When applying the foreground removal methods to uncover the faint cosmological signal from the epoch of reionization (EoR), the foreground spectra are assumed to be smooth. However, this assumption can be seriously violated in practice since the unresolved or mis-subtracted foreground sources, which are further complicated by the frequency-dependent beam effects of interferometers, will generate significant fluctuations along the frequency dimension. To address this issue, we propose a novel deep-learning-based method that uses a 9-layer convolutional denoising autoencoder (CDAE) to separate the EoR signal. After being trained on the SKA images simulated with realistic beam effects, the CDAE achieves excellent performance as the mean correlation coefficient ($\bar{\rho}$) between the reconstructed and input EoR signals reaches $0.929 \pm 0.045$. In comparison, the two representative traditional methods, namely the polynomial fitting method and the continuous wavelet transform method, both have difficulties in modelling and removing the foreground emission complicated with the beam effects, yielding only $\bar{\rho}_{\text{poly}} = 0.296 \pm 0.121$ and $\bar{\rho}_{\text{cwt}} = 0.198 \pm 0.160$, respectively. We conclude that, by hierarchically learning sophisticated features through multiple convolutional layers, the CDAE is a powerful tool that can be used to overcome the complicated beam effects and accurately separate the EoR signal. Our results also exhibit the great potential of deep-learning-based methods in future EoR experiments.