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During the last decade, there has been an explosive growth in survey data anddeep learning techniques, both of which have enabled great advances forastronomy. The amount of data from various surveys from multiple epochs with awide range of wavelengths, albeit with varying brightness and quality, isoverwhelming, and leveraging information from overlapping observations fromdifferent surveys has limitless potential in understanding galaxy formation andevolution. Synthetic galaxy image generation using physical models has been animportant tool for survey data analysis, while deep learning generative modelsshow great promise. In this paper, we present a novel approach for robustlyexpanding and improving survey data through cross survey feature translation.We trained two types of neural networks to map images from the Sloan DigitalSky 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 morphologicalinformation. We substantiate the robustness of our method by generating DESrepresentations of SDSS images from outside the overlapping region, showingthat the brightness and quality are improved even when the source images are oflower quality than the training images. Finally, we highlight several images inwhich the reconstruction process appears to have removed large artifacts fromSDSS images. While only an initial application, our method shows promise as amethod for robustly expanding and improving the quality of optical survey dataand provides a potential avenue for cross-band reconstruction.

Survey2Survey: A deep learning generative model approach for cross-survey image mapping