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

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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This repository contains the image files from Survey2Survey: a deep learning generative model approach for cross-survey image mapping. Please cite https://arxiv.org/abs/2011.07124 if you use this data in a publication. For more information, contact Brandon Buncher at buncher2(at)illinois.edu
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tutorial.ipynb demonstrates how to load the image files (uploaded here as tarballs). Images were obtained from the SDSS DR16 cutout server (https://skyserver.sdss.org/dr16/en/help/docs/api.aspx) and DES DR1 cutout server (https://des.ncsa.illinois.edu/desaccess/

./sdss_train/ and ./des_train/ contain the original SDSS and DES images used to train the neural network (Stripe82)
./sdss_test/ and ./des_test/ contain the original SDSS and DES images used for the validation dataset (Stripe82)
./sdss_ext/ contain images from the external SDSS dataset (SDSS images without a DES counterpart, outside Stripe82)
./cae and ./cyclegan contain images generated by the CAE and CycleGAN, respectively. train_decoded/ and test_decoded/ contain the reconstructions of the images from the training dataset and test dataset, respectively.  external_decoded/ contain the DES-like image reconstructions of SDSS objects from the external dataset (outside Stripe82).