papers
Publications (3)
astro-ph.GA2018
PSFGAN: a generative adversarial network system for separating quasar point sources and host galaxy light
Dominic Stark, Barthelemy Launet, Kevin Schawinski +7
The study of unobscured active galactic nuclei (AGN) and quasars depends on the reliable decomposition of the light from the AGN point source and the extended host galaxy light. Th…
astro-ph.GA2022
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…
astro-ph.GA2022
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…