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Article overview
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Deep learning insights into cosmological structure formation | | Luisa Lucie-Smith
; Hiranya V. Peiris
; Andrew Pontzen
; Brian Nord
; Jeyan Thiyagalingam
; | | Date: |
20 Nov 2020 | | Abstract: | 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.
We find no change in the predictive accuracy of the model if we retrain the
model removing anisotropic information from the inputs. This suggests that the
features learnt by the CNN are equivalent to spherical averages over the
initial conditions. Our results indicate that interpretable deep learning
frameworks can provide a powerful tool for extracting insight into cosmological
structure formation. | | Source: | arXiv, 2011.10577 | | Services: | Forum | Review | PDF | Favorites | |
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