Speaker
Description
Upcoming galaxy surveys promise to greatly inform our models of the Universe’s composition and history. Leveraging this wealth of data requires simulations that are accurate and computationally efficient. While N-body simulations set the standard for precision, their computational cost makes them impractical for large-scale data analysis. In this talk, I will present a neural network-based emulator for modelling nonlinear cosmic structure formation at the field level. Starting from the linear initial conditions of the early Universe, this model predicts the nonlinear evolution from an N-body simulation. We include the underlying cosmological parameters and time evolution, ensuring physical consistency by enforcing a key constraint: velocities correspond to time derivatives of displacements. This constraint markedly enhances the model’s accuracy. Trained on an extensive suite of N-body simulations, the network achieves remarkable precision, particularly on small, nonlinear scales where traditional approximations often struggle. The model effectively captures highly nonlinear phenomena, including dark matter halo mergers, and is computationally efficient enough for tasks such as generating mock catalogs and reconstructing the Universe’s initial conditions, as I will demonstrate with various applications. This method paves the way for robust, large-scale cosmological analyses using nonlinear scales at the field level.
| AI keywords | physics-informed neural network; surrogate model; simulation-based inference |
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