Untangling Similarities and Differences in Star Formation Histories Across Cosmological Simulations

How galaxies assemble depends on processes ranging from small-scale baryonic physics to large-scale cosmological assembly, which are difficult to disentangle with observations alone. Using about 63,000 simulated galaxies from seven cosmological simulations spanning hydrodynamical, semi-analytic, and semi-empirical models, we explore how specific star formation histories (sSFHs) vary across modelling assumptions. Using non-linear dimensionality reduction (UMAP), we find that sSFH shape variation organizes along a few smooth axes in a 2D embedding, broadly corresponding to (1) peak timing, (2) peak sharpness, and (3) subsequent quenching rate. We identify sSFH shapes that many simulations share ("common"), well described to first order by dark matter halo assembly, and a few tied to individual simulations ("unique"), which likely reflect subgrid and parametrization choices such as merger-triggered burst prescriptions. A notable exception is a common population of rapidly quenched galaxies found across most simulations, likely driven by e.g., active galactic nucleus (AGN) feedback, that halo assembly alone cannot reproduce. The origin of the most distinctive unique shape, an Illustris-dominated history with an early peak and slow decline, remains unclear, but we test the effects of short-term variability. With a multi-objective auto-encoder neural network, we recover the simulation that a given sSFH is drawn from with about 95% out-of-sample accuracy; ablation tests indicate this information is embedded in long-timescale sSFH morphology rather than short-term variability. These results provide avenues for theoretical and observational follow-up connecting star formation histories to subgrid physical models.