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 imag...

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Bibliographic Details
Published in:arXiv.org 2021-03
Main Authors: Montefalcone, Gabriele, Abitbol, Maximilian H, Kodwani, Darsh, Grumitt, R D P
Format: Article
Language:English
Subjects:
Artificial neural networks
Big Bang theory
Cosmic microwave background
Cosmology
Data analysis
Image reconstruction
Kolmogorov-Smirnov test
Masks
Neural networks
Power spectra
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2011.01433