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.
翻译:在过去十年中,调查数据和深层学习技术出现了爆炸性增长,使天文学取得了巨大进步。从多个波长范围各异、亮度和质量各异的时代进行的各种调查所产生的数据数量惊人,而从不同调查的重叠观测获得的信息在理解银河形成和演变方面有着无限的潜力。使用物理模型的合成星系图像生成是调查数据分析的一个重要工具,而深层学习基因模型则显示了巨大的前景。在本文件中,我们提出了一个通过交叉调查特征翻译大力扩展和改进调查数据的新办法。我们培训了两种神经网络,将斯隆数字天空测量(SDSS)的图像绘制成暗能量能源测量(DES)的相像。该地图用于生成SDSS图像的虚假数据描述,提高了光度和S/N,同时保留了重要的形态学信息。我们通过从重叠区域以外生成SDSS图像的DE图像描述,证实了我们的方法的稳健性,表明即使源图像的质量低于SDSS的高质量,我们也培训了两种神经网络网络,将斯隆数字天空测量(SDSS)的图像映射图图绘制成了一种前景。我们只是将改进了一种改进的图象的路径,只是将改进了SDDDIS的模型的原始图。我们用的方法。我们从一个改进了一种改进了一种方法,作为不断改进的原始图象的原始图制图。最后显示了一种改进的方法。我们将展示了一种改进了一种改进的方法。我们用的方法。我们用的方法,从一个改进了一种改进了一种改进了一种方法,从改革的方法。