In this work, we propose a new method to fill in (or in-paint) 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 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 1.83 squared degree extension, which, in our gridding, corresponds to 64 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. Finally, we show that GAN is able to mimic PDF and number density of peaks for both Gaussian and non-Gaussian data with less than 0.5% relative error.

在这项工作中，我们提出了一种新的方法，可以在点源提取过程之后，将CMB信号填充（或画图）在被掩盖的区域中。我们采用改进的生成对抗网络（GAN），比较内部（超）参数和训练策略的不同组合。我们使用合适的study _r研究性能变量，以估计有关CMB功率谱恢复的性能。我们考虑一个测试集，其中一个点源在每个天空斑块中以1.83 1.83平方度的扩展被掩盖，在我们的网格中，它对应于64 64像素。GAN经过优化，可在Planck 2018总强度模拟中估算性能。训练使GAN有效地重构了对应于约1500个像素的掩膜，误差降低了1％，直到对应于约5弧分的角标度。最后，我们证明GAN能够模拟PDF和峰值数据密度，且高斯和非高斯数据的相对误差均小于0.5％。