论文标题
使用部分卷积神经网络介绍CMB图
Inpainting CMB maps using Partial Convolutional Neural Networks
论文作者
论文摘要
我们提出了部分卷积神经网络（PCNN）的新颖应用，该应用可以注入宇宙微波背景的图像。对于覆盖图像区域的〜10％的圆形和不规则形状的口罩，该网络可以将地图和功率谱重建为几个百分比。通过执行Kolmogorov-Smirnov测试，我们表明，重建的地图和功率谱与99.9％水平的输入图和功率谱没有区别。此外，我们表明PCNN可以用规则和不规则的面膜贴上标准地图，以相同的精度。这对于来自天体物理源（例如银河前景）的CMB的涂料不规则面具特别有益。本文所示的概念验证验证表明，PCNN可以是宇宙学数据分析管道中的重要工具。
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.
