Related papers: Mock Observations: Morphological Analysis of Galax…
Context. Convolutional neural networks (CNNs) are widely used for automated galaxy morphological classification in large surveys. However, projection effects, image artefacts, and intrinsic degeneracies limit reliable identification of…
In this study, galaxy samples have been generated using mock observation techniques based on the results of TNG100-1 simulations to investigate three forms of intrinsic alignment: satellite-central alignment between the orientation of the…
We analyze the optical morphologies of galaxies in the IllustrisTNG simulation at $z\sim0$ with a Convolutional Neural Network trained on visual morphologies in the Sloan Digital Sky Survey. We generate mock SDSS images of a mass complete…
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…
The galaxy luminosity function and galaxy stellar mass function are fundamental statistics in the testing of galaxy formation models. Theoretical predictions based on cosmological simulations can deviate from observations, especially at 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…
Using the TNG50 cosmological simulation and observations from the Kilo-Degree Survey (KiDS), we investigate the connection between galaxy mergers and optical morphology in the local Universe over a wide range of galaxy stellar masses…
Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and the way the morphological types are distributed…
The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS)…
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a…
In order to understand the formation and subsequent evolution of galaxies one must first distinguish between the two main morphological classes of massive systems: spirals and early-type systems. This paper introduces a project, Galaxy Zoo,…
The study of morphology in galaxies offers a convenient and quantitative method to measure the shapes and characteristics of galaxy light distribution that reflect the evolutionary history. For AGN-host dwarf galaxies, however, there is a…
We train three convolutional neural networks (CNNs) to classify galaxies with Galaxy Zoo 2 dataset and extract the activations from the last fully connected layer or the last average pooling layer of CNNs to study the high-dimensional…
The universe is composed of galaxies that have diverse shapes. Once the structure of a galaxy is determined, it is possible to obtain important information about its formation and evolution. Morphologically classifying galaxies means…
We study the optical morphology of galaxies in a large-scale hydrodynamic cosmological simulation, the EAGLE simulation. Galaxy morphologies were characterized using non-parametric statistics (Gini, $M_{20}$, Concentration and Asymmetry)…
Large photometric surveys will image billions of galaxies, but we currently lack quick, reliable automated ways to infer their physical properties like morphology, stellar mass, and star formation rates. Simulations provide galaxy images…
We present a catalog of detailed visual classifications for 14034 galaxies in the Sloan Digital Sky Survey (SDSS) Data Release 4 (DR4). Our sample includes nearly all spectroscopically-targeted galaxies in the redshift range 0.01<z<0.1 down…
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…
Morphological classification conveys abundant information on the formation, evolution, and environment of galaxies. In this work, we refine the two-step galaxy morphological classification framework ({\tt\string USmorph}), which employs a…
We describe application of the `shapelet' linear decomposition of galaxy images to morphological classification using images of $\sim$ 3000 galaxies from the Sloan Digital Sky Survey. After decomposing the galaxies we perform a principal…