Galaxies at 0.02<z<0.1 morphological catalog

As for the classification of galaxies by their detailed structural morphological features, our CNN model gives the accuracy in the range of 83.3-99.4% depending on features, a number of galaxies with the given feature in the inference dataset, and the galaxy image quality. This allowed us, for the first time, to assign the detailed morphological classification for more than 140K low-redshift galaxies. We describe in detail the adversarial validation technique as well as how we managed the optimal train-test split of galaxies from the training data set.

We have also found optimal galaxy image transformations to increase the classifier generalization ability. It can be considered as another way to improve the human bias for those galaxy images that had a poor vote classification in the GZ project. Such an approach, likely auto- immunization, when the CNN classifier trained on very good images is able to retrain bad images from the same homogeneous sample, can be considered co-planar to other methods of combating the human bias (Khramtsov et al., 2022KNIT...28....3V).

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. 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. Galaxies at 0.02<z<0.1 morphological catalog v.2 (Vavilova et al., 2022KNIT...28....3V) is available in CSV format at ftp://ftp.mao.kiev.ua/pub/astro/cats/galaxies /galSDSSDR9zto0.1morph_classification.csv.

This Catalog is supplemented with the VizieR Online Data Catalog: SDSS galaxies morphological classification (Vavilova et al., 2021A&A...648A.122V, Cat. J/A+A/648/A122). This Catalog is also supplemented with the papers: - Machine learning technique for morphological classification of galaxies from SDSS.

II. The image-based morphological catalogs of galaxies at 0.02<z<0.1. Vavilova, I. B. ; Khramtsov, V. ; Dobrycheva, D. V. ; Vasylenko, M. Yu. ; Elyiv, A. A. ; Melnyk, O. V. Space Science and Technology, Vol. 28, No. 1, pp.03-22 (2022KNIT...28....3V). - Machine learning technique for morphological classification of galaxies from the SDSS.

III. The CNN image-based inference of detailed features. Khramtsov, V., Vavilova, I.B., Dobrycheva, D.V., Vasylenko, M.Yu., Melnyk, O.V., Elyiv, A.A., Akhmetov, V.S., Dmytrenko, A.M. Space Science and Technology, Vol. 28, No. 5, pp. 27-55 (2022KNIT...28....3V). doi.org/10.15407/knit2022.05.027