论文标题
Forse：一种基于GAN的算法，用于将CMB前景模型扩展到次级角度尺度
ForSE: a GAN based algorithm for extending CMB foreground models to sub-degree angular scales
论文作者
论文摘要
我们提出了一个新颖的Python软件包（前景尺度扩展器），旨在在宇宙微波背景实验（CMB）的背景下克服弥散银河辐射模拟中的当前局限性。 Forse利用了生成对抗神经网络（GAN）学习和再现一组图像中存在的复杂特征的能力，其目的是模拟在次级角度尺度下逼真的和非高斯的前景辐射。对于将来的CMB实验估算出对镜头重建，持久和原始B模型的前景污染的重要性，这是非常重要的。我们将此算法应用于总强度和极化中的银河热粉尘发射。我们的结果表明，Forse如何能够生成具有输入大型的小型特征（12分钟）（80分钟）。注入的结构具有统计特性，通过Minkowski功能评估，与真实天空的函数吻合，它们显示出正确的幅度缩放作为角度的函数。获得的热粉尘Stokes Q和U完整的天空图以及Forse包装套件可公开下载。
We present ForSE (Foreground Scale Extender), a novel Python package which aims at overcoming the current limitations in the simulation of diffuse Galactic radiation, in the context of Cosmic Microwave Background experiments (CMB). ForSE exploits the ability of generative adversarial neural networks (GANs) to learn and reproduce complex features present in a set of images, with the goal of simulating realistic and non-Gaussian foreground radiation at sub-degree angular scales. This is of great importance in order to estimate the foreground contamination to lensing reconstruction, de-lensing and primordial B-modes, for future CMB experiments. We applied this algorithm to Galactic thermal dust emission in both total intensity and polarization. Our results show how ForSE is able to generate small scale features (at 12 arc-minutes) having as input the large scale ones (80 arc-minutes). The injected structures have statistical properties, evaluated by means of the Minkowski functionals, in good agreement with those of the real sky and which show the correct amplitude scaling as a function of the angular dimension. The obtained thermal dust Stokes Q and U full sky maps as well as the ForSE package are publicly available for download.
