论文标题
从镜头CMB与生成对抗网络中恢复星系簇收敛
Recovering Galaxy Cluster Convergence from Lensed CMB with Generative Adversarial Networks
论文作者
论文摘要
我们提出了一种利用条件生成对抗网络（CGAN）的新方法，以从镜头CMB温度图中重建星系簇收敛。我们的模型的构建是为了强调与Caldeira等介绍的残留U-NET方法相比的结构和高频正确性。 al。 （2019）。最终，我们证明，尽管这两种模型在无噪声方向（以及在群集中心的随机偏心后）中的表现相似，但在处理用5uk/Arcmin白噪声或天文学前景（TSZ和KSZ）调加5uk/Arcmin White噪声或天文学前景的CMB时，CGAN的表现优于重新点。这种表现尤其明显在高L上，这正是重新固定在表现不足的传统方法的状态下。
We present a new method which leverages conditional Generative Adversarial Networks (cGAN) to reconstruct galaxy cluster convergence from lensed CMB temperature maps. Our model is constructed to emphasize structure and high-frequency correctness relative to the Residual U-Net approach presented by Caldeira, et. al. (2019). Ultimately, we demonstrate that while both models perform similarly in the no-noise regime (as well as after random off-centering of the cluster center), cGAN outperforms ResUNet when processing CMB maps noised with 5uK/arcmin white noise or astrophysical foregrounds (tSZ and kSZ); this out-performance is especially pronounced at high l, which is exactly the regime in which the ResUNet under-performs traditional methods.
