Robust field-level inference with dark matter halos. (arXiv:2209.06843v1 [astro-ph.CO])

We train graph neural networks on halo catalogues from Gadget N-body
simulations to perform field-level likelihood-free inference of cosmological
parameters. The catalogues contain $\lesssim$5,000 halos with masses $\gtrsim
10^{10}~h^{-1}M_\odot$ in a periodic volume of $(25~h^{-1}{\rm Mpc})^3$; every
halo in the catalogue is characterized by several properties such as position,
mass, velocity, concentration, and maximum circular velocity. Our models, built
to be permutationally, translationally, and rotationally invariant, do not
impose a minimum scale on which to extract information and are able …

arxiv astro dark matter inference