Deep learning insights into cosmological structure formation

Kavli Affiliate: Brian Nord

| First 5 Authors: Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam

| Summary:

While the evolution of linear initial conditions present in the early
universe into extended halos of dark matter at late times can be computed using
cosmological simulations, a theoretical understanding of this complex process
remains elusive. Here, we build a deep learning framework to learn this
non-linear relationship, and develop techniques to physically interpret the
learnt mapping. A three-dimensional convolutional neural network (CNN) is
trained to predict the mass of dark matter halos from the initial conditions.
N-body simulations follow the microphysical laws of gravity, whereas the CNN
model provides a simplified description of halo collapse where features are
extracted from the initial conditions through convolutions and combined in a
non-linear way to provide a halo mass prediction. We find no significant change
in the predictive accuracy of the model if we retrain it removing anisotropic
information from the inputs, suggesting that the features learnt by the CNN are
equivalent to spherical averages over the initial conditions. Despite including
all possible feature combinations that can be extracted by convolutions in the
model, the final halo mass predictions do not depend on anisotropic aspects of
the initial conditions. Our results indicate that deep learning frameworks can
provide a powerful tool for extracting physical insight into cosmological
structure formation.

| Search Query: ArXiv Query: search_query=au:”Brian Nord”&id_list=&start=0&max_results=10

Read More

Leave a Reply