Related papers: Automatic Machine Learning Framework to Study Morp…
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…
We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for $\sim 8$ million galaxies in the Hyper Suprime-Cam (HSC) Wide survey with $z \leq 0.75$ and $m \leq…
We introduce a novel machine learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and…
In order to assess the accuracy with which we can determine the morphologies of AGN host galaxies, we have simulated more than 50,000 ACS images of galaxies with z < 1.25, using image and noise properties appropriate for the GOODS survey.…
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…
We use HST/ACS images and a photometric catalog of the COSMOS field to analyze morphologies of the host galaxies of approximately 400 AGN candidates at redshifts 0.3 < z < 1.0. We compare the AGN hosts with a sample of non-active galaxies…
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), $M_{\rm BH}$,…
The ability to disentangle the light of an AGN from its host galaxy is strongly dependent on the spatial resolution and depth of the imaging. As the capabilities of imaging systems improve with time, confirming that our standard techniques…
We use machine learning techniques to investigate their performance in classifying active galactic nuclei (AGNs), including X-ray selected AGNs (XAGNs), infrared selected AGNs (IRAGNs), and radio selected AGNs (RAGNs). Using known physical…
We present results from the MOSFIRE Deep Evolution Field (MOSDEF) survey on the identification, selection biases, and host galaxy properties of 55 X-ray, IR and optically-selected active galactic nuclei (AGN) at $1.4 < z < 3.8$. We obtain…
AGNs are very powerful galaxies characterized by extremely bright emissions coming out from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine their distance to investigate…
We tested how the AGN contribution (5% - 75% of the total flux) may affect different morphological parameters commonly used in galaxy classification. We carried out all analysis at $z$,$sim$,0 and at higher redshifts that correspond to the…
We propose a new point-spread function (PSF) deconvolution algorithm for images of galaxies hosting an active galactic nucleus (AGN), designed to simultaneously enhance the spatial resolution of the host galaxy and remove the bright central…
Investigating the link between supermassive black hole and galaxy evolution requires careful measurements of the properties of the host galaxies. We perform simulations to test the reliability of a two-dimensional image-fitting technique to…
In this paper we discuss an application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine…
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…
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…
Extragalactic radio continuum surveys play an increasingly more important role in galaxy evolution and cosmology studies. While radio galaxies and radio quasars dominate at the bright end, star-forming galaxies (SFGs) and radio-quiet Active…
The study of morphology in galaxies offers a convenient and quantitative method to measure the shapes and characteristics of galaxy light distribution that reflect the evolutionary history. For AGN-host dwarf galaxies, however, there is a…
We present the analysis and results of a spectroscopic follow-up program of a mass-selected sample of six galaxies at 3 < z < 4 using data from Keck-NIRSPEC and VLT-Xshooter. We confirm the z > 3 redshifts for half of the sample through the…