Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey
This work addresses the problem of efficiently constructing galaxy catalogs for large-scale astronomical surveys like DES, representing an incremental improvement through transfer learning and automation.
The authors tackled the challenge of classifying galaxies in the Dark Energy Survey (DES) by transferring knowledge from deep learning models pre-trained on SDSS data, achieving state-of-the-art accuracy of ≥99.6% and completing the process in eight minutes using distributed training. They also used the model to label over ten thousand unlabelled DES galaxies and performed unsupervised clustering to group them into two morphological classes.
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 $\gtrsim99.6\%$. We demonstrate that this process can be completed within just eight minutes using distributed training. While this represents a significant step towards the classification of DES galaxies that overlap previous surveys, we need to initiate the characterization of unlabelled DES galaxies in new regions of parameter space. To accelerate this program, we use our neural network classifier to label over ten thousand unlabelled DES galaxies, which do not overlap previous surveys. Furthermore, we use our neural network model as a feature extractor for unsupervised clustering and find that unlabeled DES images can be grouped together in two distinct galaxy classes based on their morphology, which provides a heuristic check that the learning is successfully transferred to the classification of unlabelled DES images. We conclude by showing that these newly labeled datasets can be combined with unsupervised recursive training to create large-scale DES galaxy catalogs in preparation for the Large Synoptic Survey Telescope era.