Type: Talk
Session: Galaxies Science from Dwarfs to Clusters
Author: Aritra Ghosh

Abstract: Many unsolved challenges in extragalactic astronomy are driven by the fact that galaxy evolution is stochastic in nature. Rubin-LSST will present us with a large, uniform dataset perfect for settling these outstanding questions. I will outline how we can already leverage LSST pre-cursor datasets along with robust statistical analysis and machine-learning frameworks to unlock new insights into galaxy evolution. One such issue that has remained enigmatic with over a decade of conflicting results is the relationship between galaxy size and large-scale environmental density. Using 3 Million Hyper Suprime-Cam galaxies, I will demonstrate how we can conclusively confirm with >5-sigma confidence that galaxies in denser environments are up to 25% larger than their counterparts with similar mass and morphology in less-dense regions of the universe. Compared to most previous studies, this sample is ~1000 times larger and goes ~1 dex deeper in mass completeness. I will discuss how existing theoretical frameworks fall short in explaining the observed correlations and emphasize the need for more comprehensive investigations into the galaxy-halo connection. I will also touch upon outstanding challenges and infrastructure needs for replicating similar studies at the LSST-scale.