Using Machine Learning to Determine Morphologies of z < 1 AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey

Using Machine Learning to Determine Morphologies of z < 1 AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey

We present a machine-learning framework to accurately characterize the 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 Galaxy Morphology Network (G a M or N et ) to estima...

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Bibliographic Details
Main Authors: Chuan Tian, C. Megan Urry, Aritra Ghosh, Ryan Ofman, Tonima Tasnim Ananna, Connor Auge, Nico Cappelluti, Meredith C. Powell, David B. Sanders, Kevin Schawinski, Dominic Stark, Grant R. Tremblay
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:The Astrophysical Journal
Subjects:
Active galactic nuclei
Galaxies
Galaxy classification systems
Astronomy data analysis
Neural networks
Convolutional neural networks
Online Access:https://doi.org/10.3847/1538-4357/acad79

Internet

https://doi.org/10.3847/1538-4357/acad79

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