论文标题
使用卷积神经网络介入银河前景强度和极化图
Inpainting Galactic Foreground Intensity and Polarization maps using Convolutional Neural Network
论文作者
论文摘要
深度卷积神经网络一直是图像产生和恢复的流行工具。这些网络的性能与大型数据集学习现实功能的能力有关。在这项工作中，我们在毫米和亚毫米级的银河弥漫发射的背景下，应用了非高斯信号的问题，特别是同步尘和热灰尘发射。它们都受到半乳酸无线电源（前）和尘土飞扬的星系星系（后者）而受到小角度尺度污染的影响。我们考虑了最近的邻居介绍技术的性能，并将其与依赖生成神经网络的两种小说方法进行了比较。我们表明，生成网络能够在置信水平上更加一致地重现地面真相信号的统计特性。宇宙学和天体物理来源（毕加索）的python inpainter是编码本工作中描述的一系列介入方法的包装，并已公开可用。
Deep convolutional neural networks have been a popular tool for image generation and restoration. The performance of these networks is related to the capability of learning realistic features from a large dataset. In this work, we applied the problem of inpainting non-Gaussian signal, in the context of Galactic diffuse emissions at the millimetric and sub-millimetric regimes, specifically Synchrotron and Thermal Dust emission. Both of them are affected by contamination at small angular scales due to extra-galactic radio sources (the former) and to dusty star-forming galaxies (the latter). We consider the performances of a nearest-neighbors inpainting technique and compare it with two novels methodologies relying on generative Neural Networks. We show that the generative network is able to reproduce the statistical properties of the ground truth signal more consistently with high confidence level. The Python Inpainter for Cosmological and AStrophysical SOurces (PICASSO) is a package encoding a suite of inpainting methods described in this work and has been made publicly available.
