Related papers: How to Find Variable Active Galactic Nuclei with M…
One of the main challenges in using high redshift active galactic nuclei to study the correlations between the mass of the supermassive Black Hole (MBH) and the properties of their active host galaxies is instrumental resolution. Strong…
We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…
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…
We identified Active Galactic Nuclei (AGN) candidates as counterparts to unidentified gamma-ray sources (UGS) from the Fermi-LAT Fourth Source Catalogue at lower Galactic latitudes. Our methodology is based on the use of near- and…
We consider the techniques to distinguish normal star forming (NSF) galaxies and active galactic nuclei (AGN) hosts using optical spectra. The observational data base is a set of 20000 galaxies extracted from the Sloan Digital Sky Survey,…
The morphological classification of active galaxies may be affected by the presence of active galactic nuclei (AGN). In this paper, we provide the most detailed analysis on how different AGN contributions, from 5% to 75%, to the total…
We develop a simple method to search for changing-look (CL) active galactic nucleus (AGN) candidates, and conduct a test run. In this method, optical variations of AGNs are monitored and CL-AGNs may appear to have a pattern of being bluer…
The photoionization model of narrow-line regions (NLRs) in active galactic nuclei (AGNs) has been investigated for decades. Many published models are restricted to simple linear scaling abundance relations, dust-free assumption, uniform AGN…
In the fourth \emph{Fermi} Large Area Telescope source catalog (4FGL), 5064 $\gamma$-ray sources are reported, including 3207 active galactic nuclei (AGNs), 239 pulsars, 1336 unassociated sources, 92 sources with weak association with…
We present an estimate of the space density of Active Galactic Nuclei (AGN) at z=3. Combining deep X-ray data with Lyman Break Galaxy (LBG) colour-selection in the rest-frame UV makes for highly efficient identification of AGN in a narrow…
We present the first results of an ongoing variability monitoring program of active galactic nuclei (AGNs) using the 46\,cm telescope of the Wise observatory in Israel. The telescope has a field of view of $1.25^{\circ} \times 0.84^{\circ}$…
First principles based exploration of chemical space deepens our understanding of chemistry, and might help with the design of new materials or experiments. Due to the computational cost of quantum chemistry methods and the immens number of…
We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift…
Quantitative morphological classification of galaxies is important for understanding the origin of type frequency and correlations with environment. But galaxy morphological classification is still mainly done visually by dedicated…
We assemble a sample of 733 dwarf galaxies ($M_{\ast} \le 10^{9.5} \text{M}_\odot$) with signatures of active galactic nuclei (AGN) and explore the intersection between different AGN selection techniques. Objects in our database are…
This work develops application techniques for stochastic modelling of Active Galactic Nuclei (AGN) variability as a probe of accretion disk physics. Stochastic models, specifically Continuous Auto-Regressive Moving Average (CARMA) models,…
This study explores the space density and properties of active galaxies to z=0.8. We have investigated the frequency and nature of unresolved nuclei in galaxies at moderate redshift as indicators of nuclear activity such as Active Galactic…
Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe.…
We develop a method ('Galactic Archaeology Neural Network', GANN) based on neural network models (NNMs) to identify accreted stars in galactic discs by only their chemical fingerprint and age, using a suite of simulated galaxies from the…
Offset active galactic nuclei (AGNs) are AGNs that are in ongoing galaxy mergers, which produce kinematic offsets in the AGNs relative to their host galaxies. Offset AGNs are also close relatives of dual AGNs. We conduct a systematic search…