Related papers: Identifying AGN host galaxies by Machine Learning …
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 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…
Reliably identifying active galactic nuclei (AGNs) in dwarf galaxies is key to understanding black hole demographics at low masses and constraining models for black hole seed formation. Here we present Chandra X-ray Observatory observations…
Spitzer/IRAC color selection is a promising technique to identify hot accreting nuclei, that is to say AGN, in galaxies. We demonstrate this using a small sample of SAURON galaxies, and explore this further. The goal of this study is to…
Classifying catalog objects as stars, galaxies, or AGN is a crucial part of any statistical study of galaxies. We describe our pipeline for binary (star/galaxy) and multiclass (star/galaxy/Type I AGN/Type II AGN) classification developed…
Identifying Active Galactic Nuclei (AGNs) through their X-ray emission is efficient, but necessarily biased against X-ray-faint objects. We aim to characterize this bias by comparing X-ray-selected AGNs to the ones identified through…
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 use a combination of the XMM-Newton serendipitous X-ray survey with the optical SDSS, and the infrared WISE all-sky survey in order to check the efficiency of the low X-ray to infrared luminosity selection method in finding heavily…
Searching for active galactic nuclei (AGN) in dwarf galaxies is important for our understanding of the seed black holes that formed in the early Universe. Here, we test infrared selection methods for AGN activity at low galaxy masses. Our…
Identifying AGNs in dwarf galaxies is critical for understanding black hole formation but remains challenging due to their low luminosities, low metallicities, and star formation-driven emission that can obscure AGN signatures. Machine…
We present a new method to predict the line-of-sight column density (NH) values of active galactic nuclei (AGN) based on mid-infrared (MIR), soft, and hard X-ray data. We developed a multiple linear regression machine learning algorithm…
Optical variability has proven to be an effective way of detecting AGNs in imaging surveys, lasting from weeks to years. In the present work we test its use as a tool to identify AGNs in the VST multi-epoch survey of the COSMOS field,…
We present a machine learning model to classify Active Galactic Nuclei (AGN) and galaxies (AGN-galaxy classifier) and a model to identify type 1 (optically unabsorbed) and type 2 (optically absorbed) AGN (type 1/2 classifier). We test…
We present a study of Spitzer/IRAC and X-ray active galactic nuclei (AGNs) selection techniques in order to quantify the overlap, uniqueness, contamination, and completeness of each. We investigate how the overlap and possible contamination…
Mateos et al. (2012) presented a highly reliable and efficient mid-infrared (MIR) colour-based selection technique for luminous active galactic nuclei (AGN) using the Wide-field Infrared Survey Explorer (WISE) survey. Here we evaluate the…
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…
(abridged) The overwhelming majority of diagnostic tools for galactic activity are focused on active galaxies. Passive or dormant galaxies are often excluded from these diagnostics which usually employ emission line features. In this work,…
Identifying active galactic nuclei (AGN) is extremely important for understanding galaxy evolution and its connection with the assembly of supermassive black holes (SMBH). With the advent of deep and high angular resolution imaging surveys…
A large fraction of active galactic nuclei (AGN) are "invisible" in extant optical surveys due to either distance or dust-obscuration. The existence of this large population of dust-obscured, infrared-bright AGN is predicted by models of…
We present a spectroscopic and photometric analysis of a sample of 416,288 galaxies from the Sloan Digital Sky Survey (SDSS) matched to mid-infrared (mid-IR) data from the Wide-Field Infrared Survey Explorer (WISE). By using a new…