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

TL;DR

This paper introduces a deep learning-based method for translating images between different astronomical surveys, enhancing image quality and morphological details, which could significantly improve survey data analysis and galaxy studies.

Contribution

The paper presents a novel neural network approach for cross-survey image translation, demonstrating improved image brightness, quality, and artifact removal across different astronomical surveys.

Findings

Generated higher quality DES images from SDSS data.

Method works on images outside the training overlap region.

Shown potential for cross-band image reconstruction.

Abstract

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.