Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semi-Supervised Deep Learning
Jianzhen Chen, Zhijian Luo, Cheng Cheng, Jun Hou, Shaohua Zhang, and Chenggang Shu

TL;DR
This paper introduces a semi-supervised deep learning model, GC-SWGAN, for detecting galactic rings in large imaging surveys, achieving high accuracy and creating the largest catalog of ringed galaxies to date.
Contribution
The study develops a novel semi-supervised deep learning approach that reduces the need for extensive labeled data in galaxy ring detection, significantly improving efficiency and scale.
Findings
Achieved 97% accuracy in classifying ringed galaxies.
Identified 62,962 ringed galaxies from the DESI surveys.
Created the largest catalog of galaxy rings to date.
Abstract
The ring structures of disk galaxies are vital for understanding galaxy evolution and dynamics. However, due to the scarcity of ringed galaxies and challenges in their identification, traditional methods often struggle to efficiently obtain statistically significant samples. To address this, this study employs a novel semi-supervised deep learning model, GC-SWGAN, aimed at identifying galaxy rings from high-resolution images of the DESI Legacy Imaging Surveys. We selected over 5,000 confirmed ringed galaxies from the Catalog of Southern Ringed Galaxies (CSRG) and the Northern Ringed Galaxies from the GZ2 catalog (GZ2-CNRG), both verified by morphology expert R. J. Buta, to create an annotated training set. Additionally, we incorporated strictly selected non-ringed galaxy samples from the Galaxy Zoo 2 dataset and utilized unlabelled data from DESI Legacy Surveys to train our model.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Topological and Geometric Data Analysis
