Inpainting CMB maps using partial convolutional neural networks

We present a novel application of partial convolutional neural networks (PCNN) that can inpaint masked images of the cosmic microwave background. The network can reconstruct both the maps and the power spectra to a few percent for circular and irregularly shaped masks covering up to 10% of the image area. By performing a Kolmogorov-Smirnov test we show that the reconstructed maps and power spectra are indistinguishable from the input maps and power spectra at the 99.9% level. Moreover, we show that PCNNs can inpaint maps with regular and irregular masks to the same accuracy. This should be particularly beneficial to inpaint irregular masks for the CMB that come from astrophysical sources such as Galactic foregrounds. The proof of concept application shown in this paper shows that PCNNs can be an important tool in data analysis pipelines in cosmology.

Automated summary

This study introduces a novel application of partial convolutional neural networks (PCNN) for inpainting masked images of the cosmic microwave background, achieving map and power spectra reconstruction accuracy to a few percent with circular and irregularly shaped masks covering up to 10% of the image area. Kolmogorov-Smirnov tests demonstrate the indistinguishability of the reconstructed maps and power spectra from the input data at a 99.9% confidence level, highlighting the potential of PCNNs as an important tool in cosmological data analysis.