Related papers: Galaxies in the zone of avoidance: Misclassificati…

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…

We present the ROGER (Reconstructing Orbits of Galaxies in Extreme Regions) code, which uses three different machine learning techniques to classify galaxies in, and around, clusters, according to their projected phase-space position. We…

We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning $0 \leq z \leq 2$ from \textsc{The Three Hundred} project, a suite of…

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…

Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance of machine learning (ML) merger detection methods. We…

The Sun is located close to the Galactic mid-plane, meaning that we observe the Galaxy through significant quantities of dust. Moreover, the vast majority of the Galaxy's stars also lie in the disc, meaning that dust has an enormous impact…

Galaxy peculiar velocity data provide important dynamical clues to the structures obscured by the Zone of Avoidance (hereafter, ZOA) with resolution >~ 500km/s. This indirect probe complements the very challenging approach of directly…

A large fraction of Fermi-Large Area Telescope (LAT) sources in the fourth Fermi-LAT 14 yr catalog (4FGL) still remain unidentified (unIDed). We continued to improve our machine-learning pipeline and used it to classify 1206 X-ray sources…

We have checked the existence of a zone of avoidance oriented along the Galactic rotation axis in the globular cluster (GC) system of the Galaxy and performed a parametrization of this zone in the axisymmetric approximation. We show that an…

Thanks to incredible advances in instrumentation, surveys like the Sloan Digital Sky Survey have been able to find and catalog billions of objects, ranging from local M dwarfs to distant quasars. Machine learning algorithms have greatly…

Diagnostic diagrams of emission-line ratios have been used extensively to categorize extragalactic emission regions; however, these diagnostics are occasionally at odds with each other due to differing definitions. In this work, we study…

I review selected current observations of distant galaxies and our interpretation of the fragile (and occasionally contradictory) data. Galaxies at the ``contemporary limit'' of technology and redshift (z~6) are difficult to locate in the…

We present morphological classifications obtained using machine learning for objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes, namely early types, spirals and point sources/artifacts. An artificial neural…

We present a catalogue of galaxies in the northern Zone of Avoidance (ZoA), extracted from the shallow version of the blind HI survey with the Effelsberg 100 m radio telescope, EBHIS, that has a sensitivity of 23 mJy/beam at 10.24 km/s…

Besides its major objective tuned to the detection of the stellar galactic population the Gaia mission experiment will also observe a large number of galaxies. In this work we intend to evaluate the number and the characteristics of the…

Galaxies behind the Milky Way suffer size reduction and dimming due to their obscuration by dust in the disk of our Galaxy. The degree of obscuration is wavelength dependent. It decreases towards longer wavelengths. Compared to the optical,…

In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…

Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…

In this paper we discuss an application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine…

Classifying galaxies is an essential step for studying their structures and dynamics. Using GalaxyZoo2 (GZ2) fractions thresholds, we collect 545 and 11,735 samples in non-galaxy and galaxy classes, respectively. We compute the Zernike…