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

分类： 天文学 >> 天文学 提交时间： 2023-02-19

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

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展开 -->

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