论文标题
通过生成对抗网络为CMB数据分析介绍
Inpainting via Generative Adversarial Networks for CMB data analysis
论文作者
论文摘要
在这项工作中，我们提出了一种新方法，以在点源提取过程后掩盖的区域内注明CMB信号。我们采用改良的生成对抗网络（GAN），并比较内部（超）参数和培训策略的不同组合。我们使用合适的$ \ MATHCAL {C} _R $变量研究性能，以估算有关CMB功率频谱恢复的性能。我们考虑了一个测试集，其中一个点源在每个天空补丁中都用1.83 $ \ times $ 1.83的平方扩展名掩盖，在我们的网格中，这对应于64美元$ \ times $ 64像素。 GAN经过优化，用于估计Planck 2018总强度模拟的性能。该训练使GAN有效地重建了对应于约1500像素的掩蔽，$ 1 \％$ $误差降低到对应于约5个Arcminutes的角度尺度。
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.
