Identification of Grand-design and Flocculent Spirals from SDSS using Convolutional Neural network
Suman Sarkar, Ganesh Narayanan, Arunima Banerjee, Prem Prakash

TL;DR
This study employs a CNN to classify spiral galaxies into Grand-designs and Flocculents with high accuracy, revealing differences in mass, morphology, and bar presence, and providing insights into their formation and evolution.
Contribution
The paper introduces a CNN-based method for classifying spiral galaxies into two types, achieving 97.2% accuracy and analyzing their physical properties.
Findings
Grand-designs are generally higher mass and have lower morphological indices.
Flocculents are mostly late-type, lower mass galaxies.
Bar presence is similar across both spiral types.
Abstract
Spiral galaxies can be classified into the {\it Grand-designs} and {\it Flocculents} based on the nature of their spiral arms. The {\it Grand-designs} exhibit almost continuous and high contrast spiral arms and are believed to be driven by density waves, while the {\it Flocculents} have patchy and low-contrast spiral features and are primarily stochastic in origin. We train a convolutional neural network (CNN) model to classify spirals into {\it Grand-designs} and {\it Flocculents}, with a testing accuracy of . We then use the above model for classifying new spirals from the SDSS. Out of these, were identified as {\it Flocculents}, and the rest as {\it Grand-designs}. We find the median asymptotic rotational velocities of our newly classified {\it Grand-designs} and {\it Flocculents} are and respectively,…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Electrical and Electromagnetic Research
