Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods

TL;DR

This paper presents a machine learning approach using a two-stage Swin Transformer model to identify ring galaxies in DESI imaging surveys, significantly expanding the catalog of known ring galaxies for further astrophysical research.

Contribution

The study introduces a novel two-stage classification framework with transformer architecture to effectively detect ring galaxies in large survey data, addressing previous catalog limitations.

Findings

Identified 8052 new ring galaxies from 573,668 images.

Achieved over 64.87% precision in classification.

Demonstrated the impact of dataset imbalance on model performance.

Abstract

The formation and evolution of ring structures in galaxies are crucial for understanding the nature and distribution of dark matter, galactic interactions, and the internal secular evolution of galaxies. However, the limited number of existing ring galaxy catalogs has constrained deeper exploration in this field. To address this gap, we introduce a two-stage binary classification model based on the Swin Transformer architecture to identify ring galaxies from the DESI Legacy Imaging Surveys. This model first selects potential candidates and then refines them in a second stage to improve classification accuracy. During model training, we investigated the impact of imbalanced datasets on the performance of the two-stage model. We experimented with various model combinations applied to the datasets of the DESI Legacy Imaging Surveys DR9, processing a total of 573,668 images with redshifts ranging from z_spec = 0.01-0.20 and magr <17.5. After applying the two-stage filtering and conducting visual inspections, the overall Precision of the models exceeded 64.87%, successfully identifying a total of 8052 newly discovered ring galaxies. With our catalog, the forthcoming spectroscopic data from DESI will facilitate a more comprehensive investigation into the formation and evolution of ring galaxies.