Separating the EoR signal with a convolutional denoising autoencoder: a deep-learning-based method

doi:10.1093/mnras/stz582

Separating the EoR signal with a convolutional denoising autoencoder: a deep-learning-based method

2019-02-27

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DOI：10.1093/mnras/stz582

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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 missubtracted 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 nine-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 ((rho) over bar) between the reconstructed and input EoR signals reaches 0.929 +/- 0.045. In comparison, the two representative traditional methods, namely the polynomial fitting method and the continuous wavelet transform method, both have difficulties inmodelling and removing the foreground emission complicated with the beameffects, yielding only (rho) over bar poly = 0.296 +/- 0.121 and (rho) over bar cwt = 0.198 +/- 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 deeplearning- based methods in future EoR experiments.

Keywords:

methods: data analysis

techniques: interferometric

dark ages, reionization, first stars

radio continuum: general

Separating the EoR signal with a convolutional denoising autoencoder: a deep-learning-based method

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