Identification of Grand-design and Flocculent spirals from SDSS using deep convolutional neural network

Abstract

Spiral galaxies can be classified into the Grand-designs and Flocculents based on the nature of their spiral arms. The Grand-designs exhibit almost continuous and high contrast spiral arms and are believed to be driven by stationary density waves, while the Flocculents have patchy and low-contrast spiral features and are primarily stochastic in origin. We train a deep convolutional neural network model to classify spirals into Grand-designs and Flocculents, with a testing accuracy of $\mathrm{97.2{{\ \rm per\ cent}}}$. We then use the above model for classifying 1354 spirals from the SDSS. Out of these, 721 were identified as Flocculents, and the rest as Grand-designs. Interestingly, we find the mean asymptotic rotational velocities of our newly classified Grand-designs and Flocculents are 218 ± 86 and 146 ± 67 km s−1, respectively, indicating that the Grand-designs are mostly the high-mass and the Flocculents the intermediate-mass spirals. This is further corroborated by the observation that the mean morphological indices of the Grand-designs and Flocculents are 2.6 ± 1.8 and 4.7 ± 1.9, respectively, implying that the Flocculents primarily consist of a late-type galaxy population in contrast to the Grand-designs. Finally, an almost equal fraction of bars ∼0.3 in both the classes of spiral galaxies reveals that the presence of a bar component does not regulate the type of spiral arm hosted by a galaxy. Our results may have important implications for formation and evolution of spiral arms in galaxies.

Document Details

Document Type
Pub Defense Publication
Publication Date
Oct 27, 2022
Source ID
10.1093/mnras/stac3096

Entities

People

  • Arunima Banerjee
  • Ganesh Narayanan
  • Prem Prakash
  • Suman Sarkar

Organizations

  • Alfred P. Sloan Foundation
  • Carnegie Mellon University
  • Indian Institutes of Science Education and Research
  • Johns Hopkins University
  • Lawrence Berkeley National Laboratory
  • Leibniz Institute for Astrophysics Potsdam
  • Office of Science
  • University of Tokyo
  • University of Utah

Tags

Fields of Study

  • Physics

Readers

  • Astronomy/Astrophysics
  • Nanoscale Plasmonic Nanotechnology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks