Related papers: Fermi LAT AGN classification using supervised mach…

We systematically investigate the near- (NIR) to far-infrared (FIR) photometric properties of a nearly complete sample of local active galactic nuclei (AGN) detected in the Swift/Burst Alert Telescope (BAT) all-sky ultra hard X-ray (14-195…

The Large Area Telescope (LAT) on board the \emph{Fermi} Gamma-ray Space Telescope has been continuously providing good quality survey data of the entire sky in the high energy range from 30 MeV to 500 GeV and above since August 2008. A…

Active galactic nuclei (AGN) are supermassive black holes with luminous accretion disks found in some galaxies, and are thought to play an important role in galaxy evolution. However, traditional optical spectroscopy for identifying AGN…

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…

We present The Machine, an artificial neural network (ANN) capable of differentiating between the numbers of Gaussian components needed to describe the emission lines of Integral Field Spectroscopic (IFS) observations. Here we show the…

We are compiling a new list of gamma-ray jetted active galactic nuclei (AGN), starting from the fourth catalog of point sources of the Fermi Large Area Telescope (LAT). Our aim is to prepare a list of jetted AGN with known redshifts and…

The Second Catalog of Blazars and other Active Galactic Nuclei detected by the Fermi/LAT (2LAC) includes about 1100 sources, 886 of which comprise the Clean Sample. The general properties of the different populations of sources classified…

In this work, we make use of a supervised machine learning algorithm based on Logistic Regression (LR) to select TeV blazar candidates from the 4FGL-DR2 / 4LAC-DR2, 3FHL, 3HSP, and 2BIGB catalogs. LR constructs a hyperplane based on a…

This article discusses the integration of the Artificial Bee Colony (ABC) algorithm with two supervised learning methods, namely Artificial Neural Networks (ANNs) and Adaptive Network-based Fuzzy Inference System (ANFIS), for feature…

While emission-line flux ratio diagnostics are the most common technique for identifying active galactic nuclei (AGNs) in optical spectra, applying this approach to single fiber spectra of galaxies can omit entire subpopulations of AGNs.…

To understand the cosmic accretion history of supermassive black holes, separating the radiation from active galactic nuclei (AGNs) and star-forming galaxies (SFGs) is critical. However, a reliable solution on photometrically recognising…

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…

Active galactic nuclei (AGN) with jets seen at small viewing angles are the most luminous and abundant objects in the $\gamma$-ray sky. AGN with jets misaligned along the line-of-sight appear fainter in the sky, but are more numerous than…

An Artificial Neural Network (ANN) has been employed using a supervised back-propagation scheme to classify 2000 bright sources from the Calgary database of IRAS (Infrared Astronomy Satellite) spectra in the wavelength region of 8-23…

Active Galaxies with a jet pointing towards us, so-called blazars, play an important role in the field of high-energy astrophysics. One of the most important features in the classification scheme of blazars is the peak frequency of the…

We aim to study the effect of environment on the presence and fuelling of Active Galactic Nuclei (AGN) in massive galaxy clusters. We explore the use of different AGN detection techniques with the goal of selecting AGN across a broad range…

The first Fermi Large Area Telescope (LAT) catalogue of sources (1FHL) emitting at high energies (above 10 GeV) reports the details of 514 objects detected in the first three years of the Fermi mission. Of these, 71 were reported as…

Machine learning (ML) and deep learning (DL) techniques are increasingly used across astrophysics, enabled by the growing availability of data and improved acquisition methods. These approaches now support tasks from redshift estimation to…

Active galactic nuclei (AGN) are typically identified through their distinctive X-ray or radio emissions, mid-infrared (MIR) colors, or emission lines. However, each method captures different subsets of AGN due to signal-to-noise (SNR)…

We constrain the behavior of the radio luminosity function (RLF) of two classes of active galactic nuclei (AGN) namely AGN of low radio power (LRP) and BL Lac objects. The extrapolation of the observed steep RLFs to low power predicts a…