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Using Machine Learning to Determine Morphologies of AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey
Chuan Tian, C. Megan Urry, Aritra Ghosh +9
We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within . We first use PSFGAN to decouple host ga…
BASS XXVI: DR2 Host Galaxy Stellar Velocity Dispersions
Michael J. Koss, Benny Trakhtenbrot, Claudio Ricci +18
We present new central stellar velocity dispersions for 484 Sy 1.9 and Sy 2 from the second data release of the Swift/BAT AGN Spectroscopic Survey (BASS DR2). This constitutes the…
BASS XXII: The BASS DR2 AGN Catalog and Data
Michael J. Koss, Claudio Ricci, Benny Trakhtenbrot +32
We present the AGN catalog and optical spectroscopy for the second data release of the Swift BAT AGN Spectroscopic Survey (BASS DR2). With this DR2 release we provide 1425 optical…
BAT AGN Spectroscopic Survey XXI: The Data Release 2 Overview
Michael J. Koss, Benny Trakhtenbrot, Claudio Ricci +24
The BAT AGN Spectroscopic Survey (BASS) is designed to provide a highly complete census of the key physical parameters of supermassive black holes (SMBHs) that power local active g…
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
Aritra Ghosh, C. Megan Urry, Amrit Rau +11
We introduce a novel machine learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology…
BASS XXV: DR2 Broad-line Based Black Hole Mass Estimates and Biases from Obscuration
Julian E. Mejıa-Restrepo, Benny Trakhtenbrot, Michael J. Koss +26
We present measurements of broad emission lines and virial estimates of supermassive black hole masses () for a large sample of ultra-hard X-ray selected active galactic nu…