2023
DOI: 10.3847/1538-4357/acad79
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Using Machine Learning to Determine Morphologies of z < 1 AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey

Abstract: 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 (GaMorNet) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low (0 < z < 0.25),… Show more

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Cited by 3 publications
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“…The much weaker correlation found in our data implies that the AHA sample is not dominated by bulge or spheroidal galaxies and likely has many more disk galaxies, merging systems, or point sources. A detailed analysis of the host galaxies' morphology for each one of these sources is ongoing in the AHA collaboration (Schawinski et al 2014;Powell et al 2017;Ghosh et al 2022;Tian et al 2023).…”
Section: Cold Dust Emissionmentioning
confidence: 99%
“…The much weaker correlation found in our data implies that the AHA sample is not dominated by bulge or spheroidal galaxies and likely has many more disk galaxies, merging systems, or point sources. A detailed analysis of the host galaxies' morphology for each one of these sources is ongoing in the AHA collaboration (Schawinski et al 2014;Powell et al 2017;Ghosh et al 2022;Tian et al 2023).…”
Section: Cold Dust Emissionmentioning
confidence: 99%