Kavli Affiliate: Brian Nord
| First 5 Authors: Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam
| Summary:
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. However, a theoretical understanding of this complex process
remains elusive; in particular, the role of anisotropic information in the
initial conditions in establishing the final mass of dark matter halos remains
a long-standing puzzle. Here, we build a deep learning framework to investigate
this question. We train a three-dimensional convolutional neural network (CNN)
to predict the mass of dark matter halos from the initial conditions, and
quantify in full generality the amounts of information in the isotropic and
anisotropic aspects of the initial density field about final halo masses. We
find that anisotropies add a small, albeit statistically significant amount of
information over that contained within spherical averages of the density field
about final halo mass. However, the overall scatter in the final mass
predictions does not change qualitatively with this additional information,
only decreasing from 0.9 dex to 0.7 dex. Given such a small improvement, our
results demonstrate that isotropic aspects of the initial density field
essentially saturate the relevant information about final halo mass. Therefore,
instead of searching for information directly encoded in initial conditions
anisotropies, a more promising route to accurate, fast halo mass predictions is
to add approximate dynamical information based e.g. on perturbation theory.
More broadly, our results indicate that deep learning frameworks can provide a
powerful tool for extracting physical insight into cosmological structure
formation.
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