Inpainting via Generative Adversarial Networks for CMB data analysis
This addresses a domain-specific issue for CMB data analysis, offering an incremental improvement in inpainting accuracy for astronomical applications.
The paper tackles the problem of inpainting Cosmic Microwave Background (CMB) data in masked regions after point source extraction, using a modified Generative Adversarial Network (GAN) to achieve reconstruction with 1% error down to 5 arcminutes for masks of about 1500 pixels.
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. The GAN is optimized for estimating performance on Planck 2018 total intensity simulations. The training makes the GAN effective in reconstructing a masking corresponding to about 1500 pixels with $1\%$ error down to angular scales corresponding to about 5 arcminutes.