Automated Detection of Galactic Rings from SDSS Images
Linn Abraham, Sheelu Abraham, Ajit K. Kembhavi, N. S. Philip, A. K., Aniyan, Sudhanshu Barway, Harish Kumar

TL;DR
This paper develops a deep learning method to automatically detect galactic rings in SDSS images, creating a large catalog that facilitates studies of galaxy morphology, origin, and evolution.
Contribution
It introduces a novel deep learning approach for identifying galactic rings in SDSS data and generates a comprehensive catalog of ringed galaxies.
Findings
Achieved high accuracy and recall in ring detection
Generated a catalog of 29,420 galaxies with rings
Identified 2,087 barred galaxies with rings
Abstract
Morphological features in galaxies, like spiral arms, bars, rings, tidal tails etc. carry information about their structure, origin and evolution. It is therefore important to catalog and study such features and to correlate them with other basic galaxy properties, the environment in which the galaxies are located and their interactions with other galaxies. The volume of present and future data on galaxies is so large that traditional methods, which involve expert astronomers identifying morphological features through visual inspection, are no longer sufficient. It is therefore necessary to use AI based techniques like machine learning and deep learning for finding morphological structures quickly and efficiently. We report in this study the application of deep learning for finding ring like structures in galaxy images from the Sloan Digital Sky Survey (SDSS) data release DR18. We use a…
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Taxonomy
TopicsAstronomical Observations and Instrumentation · Astronomy and Astrophysical Research · Image and Object Detection Techniques
