Related papers: Detection of Bars in Galaxies using a Deep Convolu…
We present an application of an artificial neural network methodology to a modern wide-field sky survey Pan-STARRS1 in order to build a high-quality sample of disk galaxies visible in edge-on orientation. Such galaxies play an important…
We have measured the radial light profiles and global shapes of bars using two-dimensional 3.6 $\mu m $ image decompositions for 144 face-on barred galaxies from the Spitzer Survey of Stellar Structure in Galaxies (S4G). The bar surface…
We investigate the relations between the properties of bars and their host galaxies in a sample of 77 nearby barred galaxies, spanning a wide range of morphological types and luminosities, with 34 SB0-SBa and 43 SBab-SBc galaxies. The…
Classifying the morphologies of galaxies is an important step in understanding their physical properties and evolutionary histories. The advent of large-scale surveys has hastened the need to develop techniques for automated morphological…
We applied the image-based approach with a convolutional neural network model to the sample of low-redshifts galaxies with $-24^{m}<M_{r}<-19.4^{m}$ from the SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy…
Wide-field slitless spectroscopic galaxy surveys, such as the one performed by the upcoming Chinese Space Station Survey Telescope (CSST), are crucial for precision cosmology but present formidable data analysis challenges. Because spectra…
We present 3D-Barolo, a new code that derives rotation curves of galaxies from emission-line observations. This software fits 3D tilted-ring models to spectroscopic data-cubes and can be used with a variety of observations: from HI and…
We present a study of the bar fraction in the Coma cluster galaxies based on a sample of ~190 galaxies selected from the SDSS-DR6 and observed with the Hubble Space Telescope (HST) Advanced Camera for Survey (ACS). The unprecedented…
We have determined the fraction of barred galaxies in the H-band for a statistically well-defined sample of 186 spirals drawn from the Ohio State University Bright Spiral Galaxy survey. We find 56% of our sample to be strongly barred at H,…
This paper presents the results of a high-resolution imaging survey, using both ground-based and Hubble Space Telescope images, of a complete sample of nearby barred S0--Sa galaxies in the field, with a particular emphasis on identifying…
Deconvolution of large survey images with millions of galaxies requires to develop a new generation of methods which can take into account a space variant Point Spread Function (PSF) and have to be at the same time accurate and fast. We…
We investigate the spatial distribution of star formation (SF) within bars of nearby disk galaxies (inclination $< 65^{\circ}$) from the S$^4$G survey. We use archival GALEX far- and near-UV imaging for 772 barred galaxies. We also assemble…
Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…
We consider a machine learning algorithm to detect and identify strong gravitational lenses on sky images. First, we simulate different artificial but very close to reality images of galaxies, stars and strong lenses, using six different…
Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification…
A multi-faceted approach is described to constrain the importance of bar-driven evolution in disk galaxies, particularly bulge formation. N-body simulations are used to construct stellar kinematic bar diagnostics for edge-on systems and to…
We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…
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 exploited the sample of 315776 SDSS DR9 galaxies with absolute…
Tree species identification using bark images is a challenging problem that could prove useful for many forestry related tasks. However, while the recent progress in deep learning showed impressive results on standard vision problems, a…
DESI is a groundbreaking international project to observe more than 40 million quasars and galaxies over a 5-year period to create a 3D map of the sky. This map will enable us to probe multiple aspects of cosmology, from dark energy to…