论文标题
高分辨率CMB镜头重建，深度学习
High-Resolution CMB Lensing Reconstruction with Deep Learning
论文作者
论文摘要
下一代宇宙微波背景（CMB）调查有望通过沿视线创建质量图来提供有关原始宇宙的有价值信息。创建这些镜头收敛图的传统工具包括二次估计器和最大可能性的迭代估计器。在这里，我们应用生成对抗网络（GAN）来重建镜头收敛场。我们将结果与以前的深度学习方法（残留 - unet）进行比较，并讨论每个方法的利弊。在此过程中，我们使用各种功率光谱生成的训练集，而不是用于测试方法的训练集。
Next-generation cosmic microwave background (CMB) surveys are expected to provide valuable information about the primordial universe by creating maps of the mass along the line of sight. Traditional tools for creating these lensing convergence maps include the quadratic estimator and the maximum likelihood based iterative estimator. Here, we apply a generative adversarial network (GAN) to reconstruct the lensing convergence field. We compare our results with a previous deep learning approach -- Residual-UNet -- and discuss the pros and cons of each. In the process, we use training sets generated by a variety of power spectra, rather than the one used in testing the methods.
