Fast and Accurate Non-Linear Predictions of Universes with Deep Learning
This work provides a more efficient and accurate method for cosmologists to simulate the cosmic web, which is crucial for inferring properties of dark energy and dark matter from galaxy observations.
This paper addresses the computational challenge of modeling cosmic structure formation by developing a deep learning model that transforms fast linear predictions into fully non-linear predictions. The model achieves higher accuracy and speed compared to current state-of-the-art approximate methods, and it generalizes well to universes with different cosmological parameters.
Cosmologists aim to model the evolution of initially low amplitude Gaussian density fluctuations into the highly non-linear "cosmic web" of galaxies and clusters. They aim to compare simulations of this structure formation process with observations of large-scale structure traced by galaxies and infer the properties of the dark energy and dark matter that make up 95% of the universe. These ensembles of simulations of billions of galaxies are computationally demanding, so that more efficient approaches to tracing the non-linear growth of structure are needed. We build a V-Net based model that transforms fast linear predictions into fully nonlinear predictions from numerical simulations. Our NN model learns to emulate the simulations down to small scales and is both faster and more accurate than the current state-of-the-art approximate methods. It also achieves comparable accuracy when tested on universes of significantly different cosmological parameters from the one used in training. This suggests that our model generalizes well beyond our training set.