Analysis of Ring Galaxies Detected Using Deep Learning with Real and Simulated Data

Abstract

Understanding the formation and evolution of ring galaxies, which possess an atypical ring-like structure, is crucial for advancing knowledge of black holes and galaxy dynamics. However, current catalogs of ring galaxies are limited, as manual analysis takes months to accumulate an appreciable sample of rings. This paper presents a convolutional neural network (CNN) to identify ring galaxies from unclassified samples. A CNN was trained on 100,000 simulated galaxies, transfer learned to a sample of real galaxies, and applied to a previously unclassified dataset to generate a catalog of rings which was then manually verified. Data augmentation with a generative adversarial network (GAN) to simulate images of galaxies was also employed. The resulting catalog contains 1967 ring galaxies. The properties of these galaxies were then estimated from their photometry and compared to the Galaxy Zoo 2 catalog of rings. However, the model's precision is currently limited due to a severe imbalance of rings in real datasets, leading to a significant false-positive rate of 41.1%, which poses challenges for large-scale application in surveys imaging billions of galaxies. This study demonstrates the potential of optimizing ML pipelines with low training data for rare morphologies and underscores the need for further refinements to enhance precision for extensive surveys like the Vera Rubin Observatory Legacy Survey of Space and Time.

17 pages, 17 figures. Accepted for publication in the Astronomical Journal (AJ)

References in corpus (9)

• Array Programming with NumPy
• The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
• Star-galaxy Classification Using Deep Convolutional Neural Networks
• Astronomaly: Personalised Active Anomaly Detection in Astronomical Data
• The Formation of Intermediate Mass Black Holes in Galactic Nuclei
• Practical Galaxy Morphology Tools from Deep Supervised Representation Learning
• Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in $\sim 100,000$ SDSS and $\sim 20,000$ CANDELS Galaxies
• Galactic Rings Revisited. I. CVRHS Classifications of 3962 Ringed Galaxies from the Galaxy Zoo 2 Database
• A catalog of broad morphology of Pan-STARRS galaxies based on deep learning

Cited by in corpus (1)

• Discovery of the Polar Ring Galaxies with deep learning