Related papers: Classifying Seyfert galaxies with deep learning
We use the sample of emission-line nuclei derived from a recently completed optical spectroscopic survey of nearby galaxies to quantify the incidence of local (z = 0) nuclear activity. Consistent with previous studies, we find detectable…
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine…
We probed the relation between properties of Seyfert nuclei and morphology of their host galaxies. We selected Seyfert galaxies from the Sloan Digital Sky Survey with redshifts less 0.2 identified by the V\'{e}ron Catalog (13th). We used…
With the advent of new spectroscopic surveys from ground and space, observing up to hundreds of millions of galaxies, spectra classification will become overwhelming for standard analysis techniques. To prepare for this challenge, we…
Unified models of Seyfert galaxies, based on viewing angles, successfully explain the observed differences between type 1 and 2 Seyferts. The existence of a range in accretion rates relative to the Eddington rate (from broad-line Seyfert 1s…
The $BeppoSAX$ archive is currently the largest reservoir of high sensitivity simultaneous soft and hard-X ray data of Seyfert galaxies. From this database all the Seyfert galaxies (105 objects of which 43 are type I and 62 are type II)…
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…
In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep…
According to the unified model of active galactic nuclei, Seyfert 2 galaxies are physically the same as Seyfert 1 objects and they possess a broad-line region (BLR), but it is hidden from the observer due to their orientation. In the past…
We present a new accurate sample of narrow-line Seyfert 1 galaxies (NLS1s) in the southern hemisphere from the Six-degree Field Galaxy Survey (6dFGS). Based on the optical spectral features, 167 sources were classified as NLS1s. We derived…
High angular resolution spectroscopy obtained with the Hubble Space Telescope (HST) has revealed a remarkable population of galaxies hosting dwarf Seyfert nuclei with an unusually large broad-line region (BLR). These objects are remarkable…
In this paper we present a classification of emission-line galaxies at intermediate and high redshifts (0.52.5 for near-infrared spectra), using the Dn(4000) index as a supplementary diagnostic. Our goal is to complement the diagnostic…
We explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments and knots, according to the distribution and kinematics of dark matter (DM),…
We present a study of the hard X-ray spectrum (>15 keV) of different classes of Seyfert galaxies observed with BeppoSAX/PDS. Using hard X-ray data, we avoid absorption effects modifying the Seyfert emission and have direct access to the…
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…
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…
Line intensity mapping is emerging as a novel method that can measure the collective intensity fluctuations of atomic/molecular line emission from distant galaxies. Several observational programs with various wavelengths are ongoing and…
A galaxy's morphological features encode details about its gas content, star formation history, and feedback processes, which play important roles in regulating its growth and evolution. We use deep convolutional neural networks (CNNs) to…
We have discovered polarized broad emission lines in five type 2 Seyfert galaxies (NGC 424, NGC 591, NGC 2273, NGC 3081, and NGC 4507), establishing that these objects are type 1 Seyferts obscured by dense circumnuclear material. The…
We develop a straightforward and quantitative two-step method for spectroscopically classifying galaxies from the low signal-to-noise (S/N) optical spectra typical of galaxy redshift surveys. First, using \chi^2-fitting of characteristic…