299 citations · 402 across the 15 of their papers we have counts for
15 papers
Ukrainian Wartime Astronomy and its Prospects
Danilo Albergaria, Kateryna Frantseva, Pedro Russo +20
The Russian invasion of Ukraine damaged or compromised astronomical facilities and has prompted the displacement of researchers. A plan to restore Ukrainian astronomy, rooted in a…
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…
The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
Eleonora Di Valentino, Jackson Levi Said, Adam Riess +533
The standard model of cosmology has provided a good phenomenological description of a wide range of observations both at astrophysical and cosmological scales for several decades.…
NGC3521 as the Milky Way analogue: spectral energy distributon from UV to Radio and photometric variability
O. S. Pastoven, O. V. Kompaniiets, I. B. Vavilova +1
We studied the multiwavelength properties of NGC 3521, the Milky Way galaxy-twin, from UV- to radio, exploring the data from GALEX for UV-, SDSS for optical, 2MASS, WISE, MIPS (Spi…
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…