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-redshift 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. To determine the principal parameters of galaxy morphology defined within the GZ2 project, we classified the galaxies into five visual types (completely rounded, rounded in-between, smooth cigar-shaped, edge-on, and spiral). Using GZ2 galaxy morphology classification, we were able to define 34 morphological features of galaxies from the inference set of our SDSS DR9 sample, which do not match with the GZ2 training set. 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 were first defined for 216148 galaxies by image-based CNN classifier. For the rest of galaxies, the initial morphological classification was reassigned as in the GZ2 project. Our method shows the promising performance of morphological classification attaining >93 % of accuracy for five classes morphology prediction except the cigar-shaped (similar to 75 %) and completely rounded (similar to 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 range 92-99 % in depending on features, number of galaxies with the given feature in the inference dataset, and, of course, the galaxy image quality. As a result, for the first time we assigned 34 morphological detailed features (bar, rings, number of spiral arms, mergers, etc.) for more than 160000 low-redshift galaxies from the SDSS DR9. We demonstrate for the first time 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. The proposed CNN model allows solving a bunch of galaxy classification problems, for example, such as a quick selection of galaxies with a bar, bulge, ring, and other morphological features for their subsequent analysis.