标题： 使用部分卷积神经网络修复CMB地图 显示英文标题

标题： Inpainting CMB maps using Partial Convolutional Neural Networks

摘要： 我们提出了一种部分卷积神经网络(PCNN)的新应用，可以修复宇宙微波背景的掩码图像。该网络可以将地图和功率谱重建到几个百分比范围内，适用于覆盖图像面积多达约10%的圆形和不规则形状的掩码。通过进行Kolmogorov-Smirnov检验，我们表明重建的地图和功率谱在99.9%的水平上与输入的地图和功率谱无法区分。此外，我们展示了PCNN可以以相同的精度修复具有规则和不规则掩码的地图。这对于修复来自天体物理源（如银河系前景）的CMB不规则掩码应该特别有益。本文展示的概念验证应用表明，PCNN可以在宇宙学的数据分析流程中成为重要的工具。 显示英文摘要

摘要： 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.