基于U-Net的星系-星系强引力透镜（GGSL-UNet）。I. 从地面与空间观测的多波段图像中提取二维信息
Galaxy-Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting 2-Dimensional Information from Multi-Band Images in Ground and Space Observations
AI总结：
本文提出GGSL-UNet深度学习方法，利用U-Net从多波段图像中分离强透镜前景与背景二维信息，实现一秒内精确重建KiDS地面透镜并达到与标准工具相当的HST建模精度，还可用于测光红移估计。
摘要：
我们提出了一种新颖的深度学习方法，利用U-Net（简称GGSL-Unet）分别提取星系-星系强引力透镜事件中前景星系（偏折体）和背景系统（源）的二维流量信息。特别是，研究发现源图像的分割能够提升透镜建模的性能，尤其对于地面图像而言。通过将模拟透镜前景+背景成分与真实巡天噪声相结合来训练GGSL-Unet，我们证明它能够正确建模输入图像噪声并提取透镜信号。然而，本工作最重要的结果是GGSL-UNet能够在一秒内准确重建来自千度巡天（KiDS）的真实地面透镜系统。我们还在来自BELLS GALLERY的空间（HST）透镜上测试了GGSL-UNet，获得了与标准透镜建模工具相当的精度。最后，我们从重建的偏折体和源图像计算星等，并以此推导测光红移（photo-z），其中偏折体的测光红移与光谱红移高度一致。这第一项工作展示了生成网络在透镜搜寻、图像去噪、源分割以及强透镜系统分解与建模方面的巨大潜力。对于即将到来的地面和空间巡天，GGSL-UNet可以提供高质量图像以及几何和红移信息，用于精确透镜建模，并与经典MCMC建模相结合，在星系-星系强透镜分析中实现最佳精度。
英文摘要：
We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of Galaxy-Galaxy Strong Lensing events using U-Net (GGSL-Unet for short). In particular, the segmentation of the source image is found to enhance the performance of the lens modeling, especially for ground-based images. By combining mock lens foreground+background components with real sky survey noise to train the GGSL-Unet, we show it can correctly model the input image noise and extract the lens signal. However, the most important result of this work is that the GGSL-UNet can accurately reconstruct real ground-based lensing systems from the Kilo Degree Survey (KiDS) in one second. We also test the GGSL-UNet on space-based (HST) lenses from BELLS GALLERY, and obtain comparable accuracy of standard lens modeling tools. Finally, we calculate the magnitudes from the reconstructed deflector and source images and use this to derive photometric redshifts (photo-z), with the photo-z of the deflector well consistent with spectroscopic ones. This first work, demonstrates the great potential of the generative network for lens finding, image denoising, source segmentation, and decomposing and modeling of strong lensing systems. For the upcoming ground- and space-based surveys, the GGSL-UNet can provide high-quality images as well as geometry and redshift information for precise lens modeling, in combination with classical MCMC modeling for best accuracy in the galaxy-galaxy strong lensing analysis.
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