Abstract
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 ($0<z<0.25$), mid ($0.25<z<0.5$), high ($0.5<z<0.9$), extra
($0.9<z<1.1$) and extreme ($1.1<z<1.4$), and train our models independently in
each bin. We use PSFGAN to decompose the AGN point source light from its host
galaxy, and invoke the Galaxy Morphology Posterior Estimation Network (GaMPEN)
to estimate morphological parameters of the recovered host galaxy. We first
trained our models on simulated data, and then fine-tuned our algorithm via
transfer learning using labeled real data. To create training labels for
transfer learning, we used GALFIT to fit $\sim 20,000$ real HSC galaxies in
each redshift bin. We comprehensively examined that the predicted values from
our final models agree well with the GALFIT values for the vast majority of
cases. Our PSFGAN + GaMPEN framework runs at least three orders of magnitude
faster than traditional light-profile fitting methods, and can be easily
retrained for other morphological parameters or on other datasets with diverse
ranges of resolutions, seeing conditions, and signal-to-noise ratios, making it
an ideal tool for analyzing AGN host galaxies from large surveys coming soon
from the Rubin-LSST, Euclid, and Roman telescopes.