Identifying lopsidedness in spiral galaxies using a Deep Convolutional Neural Network

Abstract

About 30\% of disk galaxies show lopsidedness in their stellar disk. Although such a large-scale asymmetry in the disk can be primarily looked upon as a long-lived mode ($m=1$), the physical origin of the lopsidedness in the disk continues to be a puzzle. In this work, we employ a transfer-learning approach for the automated identification of lopsided galaxies using SDSS DR18 imaging by fine-tuning a Zoobot model, a deep convolutional neural network package pre-trained on the Galaxy Zoo dataset. We obtain 7,042 well-resolved, nearly face-on spiral galaxies from SDSS DR18 over the redshift range 0.01 $\le z\le 0.1$, with extinction-corrected g-band model magnitude < 16 and Petrosian radius (enclosing 90 \% of the flux) $\ge$ 3 arcsec. Out of these, we visually identify 490 lopsided and 444 symmetric galaxy samples suitable for training. The trained model achieves a testing accuracy of $(87\pm 0.02)$ \%, averaged over 10 independent trials. Using the best-performing model, we identify 3,679 lopsided and 2,429 symmetric galaxies from the remaining sample. Of these, 2,658 lopsided and 1,455 symmetric galaxies are predicted with are predicted with high prediction probability $P_{pred}\ge 0.85$. Lopsided galaxies in our predicted samples are relatively high star-forming, bluer, low-concentration (late-type), low-mass galaxies compared to the symmetric galaxies. Our study produces an usable catalogue of lopsided and symmetric galaxies, which will offer new insights into the formation of lopsidedness in disk galaxies. The dataset and the best-performing model are made publicly available through GitHub at https://github.com/bijusaha-astro/CNN_lopsided

19 pages, 9 figures, 7 tables (Accepted for publication in Astronomy \& Astrophysics)

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