Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey

Understand

The scale of ongoing and future electromagnetic surveys pose formidable challenges to classify astronomical objects.

• Pioneering efforts on this front include citizen science campaigns adopted by the Sloan Digital Sky Survey (SDSS).
• SDSS datasets have been recently used to train neural network models to classify galaxies in the Dark Energy Survey (DES) that overlap the footprint of both surveys.
• Herein, we demonstrate that knowledge from deep learning algorithms, pre-trained with real-object images, can be transferred to classify galaxies that overlap both SDSS and DES surveys, achieving state-of-the-art accuracy $\gtrsim99.6\%$.