46 citations · 94 across the 11 of their papers we have counts for
11 papers
Milky Way Near Twins (MWNeTs). I. A Hierarchical Framework for Identifying Candidate Evolutionary Counterparts of the Milky Way
I. B. Vavilova, A. M. Dmytrenko, D. V. Dobrycheva +8
Searches for Milky Way analogues traditionally rely on a limited set of present-day global properties. However, galaxies with similar morphology, stellar mass, star-formation rate,…
NGC 3521 as the Milky Way near twin: spectral energy distribution from UV to radio decameter ranges
O. V. Kompaniiets, I. B. Vavilova, Y. V. Vasylkivskyi +10
Milky Way analogues (MWAs) are usually selected from structural and kinematic properties, but robust SED-based similarity criteria are limited by heterogeneous photometry and incom…
Galaxy rotation curve based on RGB stars from the Gaia DR3 catalogue
P. N. Fedorov, A. M. Dmytrenko, V. S. Akhmetov +8
In this paper, we construct a detailed circular velocity curve of the Milky Way out to 20 kpc based on the radial component of the Jeans equation in cylindrical coordinates, assumi…
Discovery of the Polar Ring Galaxies with deep learning
D. V. Dobrycheva, O. O. Hetmantsev, I. B. Vavilova +3
The aim of our research is to create a catalog of strong and good candidates for PRGs using existing catalogs of PRGs, develop an image-based approach with machine learning methods…
Spatial orientation and shape of the velocity ellipsoids of the Gaia DR3 giants and subgiants in the Galactic plane
A. M. Dmytrenko, P. N. Fedorov, V. S. Akhmetov +8
We present the results of determining the parameters characterizing the shape and orientation of residual velocity ellipsoids from the Gaia DR3 red giants and subgiants. We show th…
An advanced approach for definition of the "Milky Way galaxies-analogues"
Iryna B. Vavilova, Petr M. Fedorov, Daria V. Dobrycheva +5
Our Galaxy - the Milky Way - has certain features of the structure and evolution. The morphological, photometric, kinematic, and chemodynamical properties are usually considered in…
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…
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…
Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach
I. B. Vavilova, D. V. Dobrycheva, M. Yu. Vasylenko +3
Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-N…