The morphological features and structural parameters of galaxies serve as a cornerstone in comprehending their attributes and have played an instrumental role in our understanding of galaxy formation and evolution. In this work, we develop novel machine learning frameworks that can determine the morphology of billion-galaxy samples expected over the next decade. We demonstrate that our frameworks can be applied across a wide variety of ground and space-based datasets, can be trained with minuscule amounts of real data, and can predict ~30-60% better uncertainties than existing morphology-determination tools.
We use our frameworks to create one of the largest structural parameter catalogs currently available, containing ~8 million Hyper Suprime-Cam galaxies. This catalog represents a significant improvement in size (~10X), depth (~4 mag), and uncertainty quantification over current state-of-the-art bulge+disk decomposition catalogs.
We use this large sample to present one of the first comprehensive studies of the variation of galaxy radius with the environment – a relationship that has remained enigmatic with over a decade of conflicting results. With >5\sigma confidence, we confirm that galaxies in denser environments are ~10-20% larger than equally massive counterparts in less dense regions of the Universe. We verify the presence of the above correlation separately for disk-dominated, bulge-dominated, star-forming, and quiescent sub-populations. We posit that the above correlation could be driven by assembly bias in dark matter halos and the varying prevalence of galaxy mergers in different environments.