Related papers: Finding active galactic nuclei through Fink

Emission from active galactic nuclei (AGNs) is known to play an important role in the evolution of many galaxies including luminous and ultraluminous systems (U/LIRGs), as well as merging systems. However, the extent, duration, and exact…

We present the first Active Galactic Nuclei (AGN) catalog in the Hobby-Eberly Telescope Dark Energy Experiment Survey (HETDEX) observed between January 2017 and June 2020. HETDEX is an ongoing spectroscopic survey with no pre-selection…

With the advent of GRAVITY+, the upgrade to the beam combiner GRAVITY at the Very Large Telescope Interferometer (VLTI), fainter and higher redshift active galactic nuclei (AGNs) are becoming observable, opening an unprecedented opportunity…

Active Galactic Nuclei (AGN) are relevant sources of radiation that might have helped reionising the Universe during its early epochs. The super-massive black holes (SMBHs) they host helped accreting material and emitting large amounts of…

Active galactic nuclei (AGNs) in the high-redshift Universe are thought to reside in overdense environments. However, recent works provide controversial results partly due to the use of different techniques and possible suppression of…

Active galactic Nuclei (AGNs) with their relativistic jets pointed toward the observer, are a class of luminous gamma-ray sources commonly known as blazars. The study of this source class is essential to unveil the physical processes…

We present a sample of 252 broad-line Active Galactic Nuclei (BLAGNs), incorporating 171 newly identified sources, spanning a redshift interval from $z$ = 0.8 to 7.2. We have analyzed spectroscopic data from the NIRSpec instrument aboard…

An empirical forward-modeling framework is developed to interpret the multiwavelength properties of Active Galactic Nuclei (AGN) and provide insights into the overlap and incompleteness of samples selected at different parts of the…

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…

Modern cosmological simulations have now matured to the point of reproducing the evolution of realistic galaxy populations across cosmic time. These simulations rely on feedback from active galactic nuclei (AGN) to quench massive galaxies,…

Both star formation (SF) and Active Galactic Nuclei (AGN) play an important role in galaxy evolution. Statistically quantifying their relative importance can be done using radio luminosity functions. Until now these relied on galaxy…

We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. The main goal of this work is to explore the potential of adaptive learning…

Active Galactic Nuclei (AGN) are remarkable astronomical sources emitting over the whole electromagnetic spectrum, with different bands providing unique windows on distinct sub-structures and their related physics. AGN come in a large…

Large time-domain sky surveys generate extensive multi-year catalogs of light curves in which scientifically valuable transients, such as supernovae (SNe), are vastly outnumbered by artifacts and routine star variability. While supervised…

We present ELDAR, a new method that exploits the potential of medium- and narrow-band filter surveys to securely identify active galactic nuclei (AGN) and determine their redshifts. Our methodology improves on traditional approaches by…

We use data from the All Wavelength Extended Groth Strip International Survey to construct stacked X-ray maps of optically bright active galaxies (AGN) and an associated control sample of galaxies at high redshift (z less than 0.6). From…

We present a highly reliable and efficient mid-infrared colour-based selection technique for luminous active galactic nuclei (AGN) using the Wide-field Infrared Survey Explorer (WISE) survey. Our technique is designed to identify objects…

We present very efficient active learning algorithms for link classification in signed networks. Our algorithms are motivated by a stochastic model in which edge labels are obtained through perturbations of a initial sign assignment…

In this paper we introduce an active learning method for symbolic regression using StackGP. The approach begins with a small number of data points for StackGP to model. To improve the model the system incrementally adds a data point such…

While it is well known that galaxies are composites of many emission processes, quantifying the various contributions remains challenging. In this work, we use unsupervised machine learning based clustering algorithms to evaluate the…