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

Abstract

We applied the image-based approach with a convolutional neural network (CNN) model to the sample of low-redshift galaxies with –24m93 % of accuracy for five classes morphology prediction except the cigar-shaped (~75 %) and completely rounded (~83 %) galaxies. 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. 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 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.