Auge, Connor
Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within $z < 1.4$ in the Hyper Supreme-Cam Wide Survey
Tian, Chuan, Urry, C. Megan, Ghosh, Aritra, Nagai, Daisuke, Ananna, Tonima T., Powell, Meredith C., Auge, Connor, Mishra, Aayush, Sanders, David B., Cappelluti, Nico, Schawinski, Kevin
We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within $z<1.4$ and $m<23$ in the Hyper Supreme-Cam Wide survey. We divide the data into five redshift bins: low ($0
Using Machine Learning to Determine Morphologies of $z<1$ AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey
Tian, Chuan, Urry, C. Megan, Ghosh, Aritra, Ofman, Ryan, Ananna, Tonima Tasnim, Auge, Connor, Cappelluti, Nico, Powell, Meredith C., Sanders, David B., Schawinski, Kevin, Stark, Dominic, Tremblay, Grant R.
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