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2020

Inpainting via Generative Adversarial Networks for CMB data analysis

Sadr, Alireza Vafaei, Farsian, Farida

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In this work, we propose a new method to inpaint the CMB signal in regions masked out following a point source extraction process.

We adopt a modified Generative Adversarial Network (GAN) and compare different combinations of internal (hyper-)parameters and training strategies.
We study the performance using a suitable $\mathcal{C}_r$ variable in order to estimate the performance regarding the CMB power spectrum recovery.
We consider a test set where one point source is masked out in each sky patch with a 1.83 $\times$ 1.83 squared degree extension, which, in our gridding, corresponds to 64 $\times$ 64 pixels.

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