272 citations · 816 across the 37 of their papers we have counts for
37 papers
Machine learning technique for morphological classification of galaxies from the SDSS. III. Image-based inference of detailed features
V. Khramtsov, I. B. Vavilova, D. V. Dobrycheva +5
This paper follows series of our works on the applicability of various machine learning methods to the morphological galaxy classification (Vavilova et al., 2021, 2022). We exploit…
Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1
I. B. Vavilova, V. Khramtsov, D. V. Dobrycheva +3
We applied the image-based approach with a convolutional neural network model to the sample of low-redshifts galaxies with from the SDSS DR9. We divided i…
Masses of Isolated Spiral KIG Galaxies
Karachentseva V. E., Karachentsev I. D., Melnyk O.
We have updated the classification of late-type galaxies presented in the Catalog of Isolated Galaxies (KIG) using the advanced digital sky surveys. Our search for companions aroun…
Early-Type (E, S0) Galaxies in the Catalog of Isolated Galaxies (KIG)
V. E. Karachentseva, I. D. Karachentsev, O. V. Melnyk
We use the data of modern digital sky surveys (PanSTARRS-1, SDSS) combined with HI-line and far ultraviolet (GALEX) surveys to reclassify 165 early-type galaxies from the Catalog o…
The Voronoi tessellation method in astronomy
Irina Vavilova, Andrii Elyiv, Daria Dobrycheva +1
The Voronoi tessellation is a natural way of space segmentation, which has many applications in various fields of science and technology, as well as in social sciences and visual a…
Machine-learning computation of distance modulus for local galaxies
A. Elyiv, O. Melnyk, I. Vavilova +2
Quickly growing computing facilities and an increasing number of extragalactic observations encourage the application of data-driven approaches to uncover hidden relations from ast…