Reconstructing galaxy formation using observational tracers: from the Milky Way to the local Universe
Abstract
In this talk I will describe two complementary approaches to understanding the assembly of galaxies in a cosmological context. In the first method, we use star cluster properties to statistically reconstruct the assembly histories of the MW and nearby galaxies. Using the E-MOSAICS simulations to follow the formation and co-evolution of ~1000 galaxies and their star clusters, we can predict the detailed merger tree of the MW from the Gaia kinematics, and are now extending the method to external galaxies using supervised machine learning to classify clusters into accreted and in-situ populations, and to identify their galactic progenitors. Assessing the performance using the known origin of the MW clusters, we obtain a GC origin classification accuracy of ~90% for 2/3 of the sample and successfully identify accreted debris buried deep within the Galaxy. In the second method, we study high quality galaxy rotation curves to understand the impact of the large-scale environment on the structure of their host dark matter (DM) haloes. Galaxies in high density environments show a systematic shift in their DM density profile at large radii that is consistent with a relatively early assembly of their host haloes. The effect is manifest in the well known radial acceleration relation (RAR) as a slight downturn at the lowest accelerations for galaxies in dense environments. This environmental dependence can be understood in the context of assembly bias within the ΛCDM cosmological paradigm, implying that the RAR can provide useful constraints on the fundamental relation between dark and luminous matter.