  Planet formation simulations are capable of directly integrating the
evolution of hundreds to thousands of planetary embryos and planetesimals, as
they accrete pairwise to become planets. In principle such investigations allow
us to better understand the final configuration and geochemistry of the
terrestrial planets, as well as to place our solar system in the context of
other exosolar systems. These simulations, however, classically prescribe
collisions to result in perfect mergers, but computational advances have begun
to allow for more complex outcomes to be implemented. Here we apply machine
learning to a large but sparse database of giant impact studies, streamlining
simulations into a classifier of collision outcomes and a regressor of
accretion efficiency. The classifier maps a 4-Dimensional parameter space
(target mass, projectile-to-target mass ratio, impact velocity, impact angle)
into the four major collision types: merger, "graze-and-merge", "hit-and-run",
and disruption. The definition of the four regimes and their boundary is fully
data-driven; the results do not suffer from any model assumption in the
fitting. The classifier maps the structure of the parameter space and provides
insights about the outcome regimes. The regressor is a neural network which is
trained to closely mimic the functional relationship between the 4-D space of
collision parameters, and a real-variable outcome, the mass of the largest
remnant. This work is a prototype of a more complete surrogate model, based on
extended sets of simulations ("big data"), that will quickly and reliably
predict specific collision outcomes for use in realistic N-body dynamical
studies of planetary formation.
