Untangling Similarities and Differences in Star Formation Histories Across Cosmological Simulations
Abstract
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 ∼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 ∼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.
Related
- Learning the Universe with PRFM-vol: Introducing a new subgrid model for star formation in cosmological simulations
- Understanding the formation of alpha-Fe bimodality in the Milky Way using constrained cosmological simulations of the Local Group
- From Cosmological Simulations to Binary Black Hole Mergers: The Impact of Using Analytical Star Formation History Models on Gravitational-wave Source Populations
- The DARWIN Project: Cosmological Simulations of Galaxy Formation down to Dwarf Scales in Diverse Environments
- The Lifecycle and Emission Properties of PAHs in Cosmological Hydrodynamic Galaxy Formation Simulations
BibTeX
@misc{savelli2026untangling,
title = {{Untangling Similarities and Differences in Star Formation Histories Across Cosmological Simulations}},
author = {Alicia M. Savelli and Matthew A. Kustec and Joshua S. Speagle and Kartheik G. Iyer and Eshal Ahmed and Zehao Peng and Norman Murray and Cissy Kuang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.17359},
eprint = {2610.17359},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.17359},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Untangling Similarities and Differences in Star Formation Histories Across Cosmological Simulations
AU - Alicia M. Savelli
AU - Matthew A. Kustec
AU - Joshua S. Speagle
AU - Kartheik G. Iyer
AU - Eshal Ahmed
AU - Zehao Peng
AU - Norman Murray
AU - Cissy Kuang
PY - 2026
DA - 2026/10/12
PB - arXiv
DO - 10.48550/arxiv.2610.17359
UR - https://arxiv.org/abs/2610.17359
AB - 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 ∼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 ∼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.
ER -