Kavli Affiliate: Brian Nord
| First 5 Authors: Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam
| Summary:
The density profiles of dark matter halos are typically modeled using
empirical formulae fitted to the density profiles of relaxed halo populations.
We present a neural network model that is trained to learn the mapping from the
raw density field containing each halo to the dark matter density profile. We
show that the model recovers the widely-used Navarro-Frenk-White (NFW) profile
out to the virial radius, and can additionally describe the variability in the
outer profile of the halos. The neural network architecture consists of a
supervised encoder-decoder framework, which first compresses the density inputs
into a low-dimensional latent representation, and then outputs $rho(r)$ for
any desired value of radius $r$. The latent representation contains all the
information used by the model to predict the density profiles. This allows us
to interpret the latent representation by quantifying the mutual information
between the representation and the halos’ ground-truth density profiles. A
two-dimensional representation is sufficient to accurately model the density
profiles up to the virial radius; however, a three-dimensional representation
is required to describe the outer profiles beyond the virial radius. The
additional dimension in the representation contains information about the
infalling material in the outer profiles of dark matter halos, thus discovering
the splashback boundary of halos without prior knowledge of the halos’
dynamical history.
| Search Query: ArXiv Query: search_query=au:”Brian Nord”&id_list=&start=0&max_results=10