Related papers: How to Find Variable Active Galactic Nuclei with M…

Variability studies hold information on otherwise unresolvable regions in Active Galactic Nuclei (AGN). Population studies of large samples likewise have been very productive for our understanding of AGN. These two themes are coming…

Measuring the redshift of active galactic nuclei (AGNs) requires the use of time-consuming and expensive spectroscopic analysis. However, obtaining redshift measurements of AGNs is crucial as it can enable AGN population studies, provide…

The use of Artificial Neural Networks (ANNs) as a classifier of digital spectra is investigated. Using both simulated and real data, it is shown that neural networks can be trained to discriminate between the spectra of different classes of…

As wide-field optical surveys such as Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) begin operations, time-domain astronomy is facing a data revolution, paving the road for new, expanded variability studies. This work…

The extremes of Active Galactic Nuclei (AGN) variability offer valuable new insights into the drivers and physics of AGN. We discuss some of the most extreme cases of AGN variability; the highest amplitudes, deep minima states, extreme…

We investigate the physical nature of active galactic nuclei (AGNs) using machine learning (ML) tools. We show that the redshift, $z$, bolometric luminosity, $L_{\rm Bol}$, central mass of the supermassive black hole (SMBH), $M_{\rm BH}$,…

We present results of recurrence analysis of 46 active galactic nuclei (AGN) using light curves from the 157-month catalog of the Swift Burst Alert Telescope (BAT) in the 14-150 keV band. We generate recurrence plots and compute recurrence…

Changing look active-galatic-nuclei (CL AGNs) can yield considerable insight into accretion physics as well as the co-evolution of black holes and their host galaxies. A large sample of these CL AGNs is essential to achieve the latter goal.…

We present a large sample of infrared-luminous candidate active galactic nuclei (AGNs) that lack X-ray detections in Chandra, XMM-Newton, and NuSTAR fields. We selected all optically detected SDSS sources with redshift measurements,…

Low Luminosity Active Galactic Nuclei (LLAGNs) are contaminated by the light of their host galaxies, thus they cannot be detected by the usual colour techniques. For this reason their evolution in cosmic time is poorly known. Variability is…

We use the Random Forest (RF) algorithm to develop a tool for automated activity classification of galaxies into 5 different classes: Star-forming (SF), AGN, LINER, Composite, and Passive. We train the algorithm on a combination of mid-IR…

The 4 Ms Chandra Deep Field-South (CDF-S) and other deep X-ray surveys have been highly effective at selecting active galactic nuclei (AGN). However, cosmologically distant low-luminosity AGN (LLAGN) have remained a challenge to identify…

We present a new method of predicting the ages of galaxies using a machine learning (ML) algorithm with the goal of providing an alternative to traditional methods. We aim to match the ability of traditional models to predict the ages of…

We present the results of a study of different statistical methods currently used in the literature to analyse the (micro)variability of active galactic nuclei (AGNs) from ground-based optical observations. In particular, we focus on the…

The survey of the COSMOS field by the VLT Survey Telescope is an appealing testing ground for variability studies of active galactic nuclei (AGN). With 54 r-band visits over 3.3 yr and a single-visit depth of 24.6 r-band mag, the dataset is…

XMM-Newton provides unprecedented insight into the X-ray Universe, recording variability information for hundreds of thousands of sources. Manually searching for interesting patterns in light curves is impractical, requiring an automated…

We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within $z<1$. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the…

Classifying Active Galactic Nuclei (AGN) is a challenge, especially for BL Lac Objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from the 4th Fermi Catalog, Data…

We investigate the use of optical variability to identify and study Active Galactic Nuclei (AGN) in the GOODS-South field. A sample of 22 mid-infrared power law sources and 102 X-ray sources with optical counterparts in the HST ACS images…

Extensive astronomical surveys, like those conducted with the {\em Chandra} X-ray Observatory, detect hundreds of thousands of unidentified cosmic sources. Machine learning (ML) methods offer an efficient, probabilistic approach to classify…