Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1

1Vavilova, IB, 2Khramtsov, V, 1Dobrycheva, DV, 1Vasylenko, MYu., 1Elyiv, AA, 1Melnyk, OV
1Main Astronomical Observatory of the National Academy of Sciences of Ukraine, Kyiv, Ukraine
2V.N. Karazin Kharkiv National University, Kharkiv, Ukraine
Space Sci. & Technol. 2022, 28 ;(1):03-22
Publication Language: English
We applied the image-based approach with a convolutional neural network model to the sample of low-redshift galaxies with –24m < Mr < –19.4m 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 (~75 %) and completely rounded (~83 %) galaxies. Main results are presented in the catalog of 27378 completely rounded, 59194 rounded in-between, 18862 cigar-shaped, 7831 edge-on, 23119 spiral in the inference data set 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 mr < 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.
Keywords: convolutional neural networks; galaxies: general, galaxy catalogues, large-scale structure of the Universe, machine learning, Methods: data analysis, morphological classification

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