Related papers: How to Find Variable Active Galactic Nuclei with M…
During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets…
Variability has proven to be a powerful tool to detect active galactic nuclei (AGN) in multi-epoch surveys. The new-generation facilities expected to become operational in the next few years will mark a new era in time-domain astronomy and…
There is an obvious need for automated classification of galaxies, as the number of observed galaxies increases very fast. We examine several approaches to this problem, utilising {\em Artificial Neural Networks} (ANNs). We quote results…
We discuss the very different methods in each wavelength band for selecting and finding Active Galactic Nuclei (AGN). We briefly review the history of the different techniques for finding AGN and compare and contrast the advantages and…
With the advancement of technology, machine learning-based analytical methods have pervaded nearly every discipline in modern studies. Particularly, a number of methods have been employed to estimate the redshift of gamma-ray loud active…
With upcoming all sky surveys such as LSST poised to generate a deep digital movie of the optical sky, variability-based AGN selection will enable the construction of highly-complete catalogs with minimum contamination. In this study, we…
Context. Active galactic nuclei (AGN) of galaxies play an important role in the life and evolution of galaxies due to the impact they exert on certain properties and the evolutionary path of galaxies. It is well known that infrared (IR)…
We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within $z<1.4$ and $m<23$ in the…
We present second epoch optical spectra for 30 changing-look (CL) candidates found by searching for Type-1 optical variability in a sample of active galactic nuclei (AGNs) spectroscopically classified as Type 2. We use a random-forest-based…
We study variability of active galactic nuclei (AGNs) by using the deep optical multiband photometry data obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) survey in the COSMOS field. The images analyzed here were taken…
Recently, machine learning methods presented a viable solution for automated classification of image-based data in various research fields and business applications. Scientists require a fast and reliable solution to be able to handle the…
We used data from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) to study the incidence of AGN in continuum-selected galaxies at $z\sim3$. From optical and infrared imaging in the 24 deg$^{2}$ Spitzer HETDEX Exploratory Large…
Variability detected in galaxies is usually attributed to their active galactic nuclei (AGNs). While all AGNs are intrinsically variable, the AGN unification model predicts that type~2 AGNs rarely vary because their engines are blocked by…
Continuum and emission-line variability of active galactic nuclei provides a powerful probe of microarcsecond scale structures in the central regions of these sources. In this contribution, we review basic concepts and methodologies used in…
Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data…
The growing volume of data produced by large astronomical surveys necessitates the development of efficient analysis techniques capable of effectively managing high-dimensional datasets. This study addresses this need by demonstrating some…
A key feature of active galactic nuclei (AGN) is their variability across all wavelengths. Typically, AGN vary by a few tenths of a magnitude or more over periods lasting from hours to years. By contrast, extreme variability of AGN -- large…
The variability of active galactic nuclei (AGN) has long held the promise of shedding light on their detailed structure, and possibly other astrophysical phenonema. Different emission mechanisms lead to different patterns of variability in…
We present the properties of active galactic nuclei (AGN) selected by optical variability in the Subaru/XMM-Newton Deep Field (SXDF). Based on the locations of variable components and light curves, 211 optically variable AGN were reliably…
(Abridged)We explore the potential of optical variability selection methods to identify AGN, including those challenging to detect with conventional techniques. Using the unprecedented combination of depth, sky coverage, and cadence of the…