Application of 3D U-Net-based neutral hydrogen foreground reduction method

Deep learning techniques are gradually making their way into the field of cosmology, bringing new ideas to the foreground subtraction of cosmological signals. This study aims to separate neutral hydrogen signals from the foreground. Traditional methods include principal component analysis (PCA) and singular value decomposition (SVD), but it is not realistic to use these methods alone to completely separate the foreground from the 21 centimeter signal in the universe, Therefore, we use a deep learning method, namely the 3D U-Net network to facilitate separation. We add residual modules to 3D U-Net to avoid network degradation issues, and also considered system effects in the foreground of 3D U-Net subtraction. We use a Gaussian beam model and use power spectrum analysis to compare the results. The analysis of the simulated data training results is shown in Fig. 8. It has been proven that using PCA processing followed by 3D U-Net for foreground subtraction has better consistency than using PCA alone. Therefore, the use of deep learning methods provides a new and effective method for observing neutral hydrogen (HI) to eliminate HI foreground and reveal physical phenomena in the universe.

Automated summary

This study utilizes the 3D U-Net network in deep learning techniques for foreground subtraction in cosmological signals. By adding residual modules and considering system effects, better consistency results have been achieved.