  Asteroids and other small bodies in the solar system tend to have irregular
shapes, owing to their low gravity. This irregularity does not only apply to
the topology, but also to the underlying geology, potentially containing
regions of different densities and materials. The topology can be derived from
optical observations, while the mass density distribution of an object is only
observable, to some extent, in its gravitational field.
  In a companion paper, we presented geodesyNets, a neural network approach to
infer the mass density distribution of an object from measurements of its
gravitational field. In the present work, we apply this approach to the
asteroid Bennu using real data from the Osiris Rex mission. The mission
measured the trajectories of not only the Osiris Rex spacecraft itself, but
also of numerous pebble-sized rock particles which temporarily orbited Bennu.
  From these trajectory data, we obtain a representation of Bennu's mass
density and validate it by propagating, in the resulting gravity field,
multiple pebbles not used in the training process. The performance is
comparable to that of a polyhedral gravity model of uniform density, but does
not require a shape model. As little additional information is needed, we see
this as a step towards autonomous on-board inversion of gravitational fields.
