Tractable Generative Modelling of Cosmological Numerical Simulations
Amit Parag, Vaishak Belle · 2025
Cosmological simulations aim to understand the matter distribution in the universe by employing either semi-analytic methods or hydrodynamical models of matter distribution. These simulations describe the evolution of baryonic structures within dark matter potential wells, where dark matter is modeled as a self-gravitating, collisionless system. Despite advances in reducing computational costs, these simulations still require millions of CPU hours to achieve stable solutions. This raises the question: can generative models predict galaxy properties from a partial history of their dynamical evolution? Tractable probabilistic models, such as sum-product networks, enable efficient computation of conditional probabilities, allowing conditional marginals to be computed in time linear to the model size. In this work, we investigate the application of sum-product networks to compactly represent and learn distributions for predictions in concordance cosmology. Using the Eagle suite of cosmological hydrodynamical simulations, we demonstrate that these graphical models can effectively reproduce mock galaxy catalogs, capturing the relationship between baryonic and dark matter with promising accuracy.