摘要: 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 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
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分类：
天文学
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天文学
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引用：
ChinaXiv:202303.03707
(或此版本
ChinaXiv:202303.03707V1)
DOI:10.12074/202303.03707V1
CSTR:32003.36.ChinaXiv.202303.03707.V1
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科创链TXID：
cc4b0096-f661-4246-a04d-91fe33d26419
- 推荐引用方式：
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.Using Machine Learning to Determine Morphologies of $z<1$ AGN Host
Galaxies in the Hyper Suprime-Cam Wide Survey.中国科学院科技论文预发布平台.[ChinaXiv:202303.03707V1]
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