Identification of Grand-design and Flocculent Spirals from SDSS using Convolutional Neural network

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 $\mathrm{97.2\%}$. We then use the above model for classifying $\mathrm{1,354}$ new spirals from the SDSS. Out of these, $\mathrm{721}$ 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 $218 \pm 86$ and $145 \pm 67$ respectively, indicating that the {\it Grand-designs} are mostly the high-mass and the {\it Flocculents} the intermediate-mass spirals. This is further corroborated by the observation that the median morphological indices of the {\it Grand-designs} and {\it Flocculents} are $2.6 \pm 1.8$ and $4.7 \pm 1.9$ respectively, implying that the {\it Flocculents} primarily consist of a late-type galaxy population in contrast to the {\it Grand-designs}. Finally, an almost equal fraction of of bars $\sim$ 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.