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This paper follows a series of our works on the applicability of various machine learning methods to morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of ~315800 low-redshift SDSS DR9 galaxies with absolute stellar magnitudes of −24m < Mr < −19.4m at 0.003 < z < 0.1 redshifts as a target data set for the CNN classifier. Because it is tightly overlapped with the Galaxy Zoo 2 (GZ2) sample, we use these annotated data as the training data set to classify galaxies into 34 detailed features. In the presence of a pronounced difference in visual parameters between galaxies from the GZ2 training data set and galaxies without known morphological parameters, we applied novel procedures, which allowed us for the first time to get rid of this difference for smaller and fainter SDSS galaxies with mr < 17.7. 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 to verify our CNN model based on the DenseNet-201 realistically. We have also found optimal galaxy image transformations, which help increase the classifier’s generalization ability. We demonstrate for the first time that implication of the CNN model with a train-test split of data sets and size-changing function simulating a decrease in magnitude and size (data augmentation) significantly improves the classification of smaller and fainter SDSS galaxies. 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, like autoimmunization, when the CNN classifier, trained on very good galaxy images, is able to retrain bad images from the same homogeneous sample, can be considered co-planar to other methods of combating such a human bias. The most promising result is related to the CNN prediction probability in the classification of detailed features. The accuracy of the CNN classifier is in the range of 83.3—99.4 % depending on 32 features (exception is for “disturbed” (68.55 %) and “arms winding medium” (77.39 %) features). As a result, for the first time, we assigned the detailed morphological classification for more than 140000 low-redshift galaxies, especially at the fainter end. A visual inspection of the samples of galaxies with certain morphological features allowed us to reveal typical problem points of galaxy image classification by shape and features from the astronomical point of view. The morphological catalogs of low-redshift SDSS galaxies with the most interesting features are available through the UkrVO website (http://ukr-vo.org/galaxies/) and VizieR.

We present the results of the kinematic investigations carried out with the use of spatial velocities of red giants and sub-giants containing in the Gaia EDR3 catalogue. The twelve kinematic parameters of the Ogorodnikov–Milne model have been derived for stellar systems with radii 0.5 and 1.0 kpc, located along the direction the Galactic center – the Sun – the Galactic anticenter within the range of Galactocentric distances R 0–8–16 kpc. By combining some of the local parameters the information related to the Galaxy as a whole has been received in the distance range 4–12 kpc, in particular circular velocity curve of red giant and sub-giants centroids, its slope, velocity gradients. We show that when using this approach, there is an alternative possibility to infer the behaviour of circular velocity curve of red giant and sub-giants centroids and its slope without using the Galactocentric distance R⊙. The kinematic parameters derived within the Solar vicinity of 1 kpc radius are in good agreement with those given in literature.

In this paper we presented the algorithm designed to efficient coordinate cross-match of objects in the modern massive astronomical catalogues. Preliminary data sort in the existed catalogues provides the opportunity for coordinate identification of the objects without any constraints with the storage and technical environment (PC). Using the multithreading of the modern computing processors allows speeding up the program up to read-write data to the storage. Also the paper contains the main difficulties of implementing of the algorithm, as well as their possible solutions.