Machine learning technique for morphological classification of galaxies
  from SDSS. II. The image-based morphological catalogs of galaxies at
  0.02<z<0.1
  We applied the image-based approach with a convolutional neural network model
to the sample of low-redshifts galaxies with $-24^{m}<M_{r}<-19.4^{m}$ from the
SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy
Zoo 2 (GZ2) dataset, considering them as the inference and training datasets,
respectively. As a result, we created the morphological catalog of 315782
galaxies at 0.02<z<0.1, where morphological five classes and 34 detailed
features (bar, rings, number of spiral arms, mergers, etc.) were first defined
for 216148 galaxies (inference dataset) by the image-based CNN classifier. For
the rest of galaxies the initial morphological classification was re-assigned
as in the GZ2 project.
  Our method shows the promising performance of morphological classification
attaining more 93 % of accuracy for five classes morphology prediction except
the cigar-shaped (75 %) and completely rounded (83 %) galaxies. Main results
are presented in the catalog of 19468 completely rounded, 27321 rounded
in-between, 3235 cigar-shaped, 4099 edge-on, 18615 spiral, and 72738 general
low-redshift galaxies of the studied SDSS sample. As for the classification of
galaxies by their detailed structural morphological features, our CNN model
gives the accuracy in the range of 92-99 % depending on features, a number of
galaxies with the given feature in the inference dataset, and the galaxy image
quality. We demonstrate that implication of the CNN model with adversarial
validation and adversarial image data augmentation improves classification of
smaller and fainter SDSS galaxies with $m_{r}$ <17.7.
