Survey2Survey：一种用于交叉调查图像映射的深度学习生成模型方法
Survey2Survey: A deep learning generative model approach for cross-survey image mapping
摘要：
在过去的十年中，调查数据和深度学习技术出现了爆炸性增长，这两者都为天文学带来了巨大的进步。来自多个时期的各种调查的数据量具有广泛的波长，尽管亮度和质量各不相同，但数量是压倒性的，并且利用来自不同调查的重叠观测的信息在理解星系形成和演化方面具有无限的潜力。使用物理模型生成合成星系图像一直是测量数据分析的重要工具，而深度学习生成模型显示出巨大的前景。在本文中，我们提出了一种新的方法，通过跨调查功能翻译强大的扩展和改善调查数据。我们训练了两种类型的神经网络，将斯隆数字巡天（SDSS）的图像映射到暗能量巡天（DES）的相应图像。该图用于生成SDSS图像的错误DES表示，增加亮度和S/N，同时保留重要的形态信息。我们证实了我们的方法的鲁棒性生成DES表示的SDSS图像从重叠区域外，显示的亮度和质量得到改善，即使当源图像的质量低于训练图像。最后，我们突出几个图像中的重建过程似乎已经从SDSS图像中删除了大的伪影。虽然只是一个初步的应用程序，我们的方法显示出作为一种方法，强大的扩展和提高光学测量数据的质量，并提供了一个潜在的途径跨带重建的承诺。
英文摘要：
During the last decade, there has been an explosive growth in survey data and deep learning techniques, both of which have enabled great advances for astronomy. The amount of data from various surveys from multiple epochs with a wide range of wavelengths, albeit with varying brightness and quality, is overwhelming, and leveraging information from overlapping observations from different surveys has limitless potential in understanding galaxy formation and evolution. Synthetic galaxy image generation using physical models has been an important tool for survey data analysis, while deep learning generative models show great promise. In this paper, we present a novel approach for robustly expanding and improving survey data through cross survey feature translation. We trained two types of neural networks to map images from the Sloan Digital Sky Survey (SDSS) to corresponding images from the Dark Energy Survey (DES). This map was used to generate false DES representations of SDSS images, increasing the brightness and S/N while retaining important morphological information. We substantiate the robustness of our method by generating DES representations of SDSS images from outside the overlapping region, showing that the brightness and quality are improved even when the source images are of lower quality than the training images. Finally, we highlight several images in which the reconstruction process appears to have removed large artifacts from SDSS images. While only an initial application, our method shows promise as a method for robustly expanding and improving the quality of optical survey data and provides a potential avenue for cross-band reconstruction.
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