  Several technologies have been proposed for deflecting a hazardous Solar
System object on a trajectory that would otherwise impact the Earth. The
effectiveness of each technology depends on several characteristics of the
given object, including its orbit and size. The distribution of these
parameters in the likely population of Earth-impacting objects can thus
determine which of the technologies are most likely to be useful in preventing
a collision with the Earth. None of the proposed deflection technologies has
been developed and fully tested in space. Developing every proposed technology
is currently prohibitively expensive, so determining now which technologies are
most likely to be effective would allow us to prioritize a subset of proposed
deflection technologies for funding and development. We present a new model,
the Deflector Selector, that takes as its input the characteristics of a
hazardous object or population of such objects and predicts which technology
would be able to perform a successful deflection. The model consists of a
machine-learning algorithm trained on data produced by N-body integrations
simulating the deflections. We describe the model and present the results of
tests of the effectiveness of nuclear explosives, kinetic impactors, and
gravity tractors on three simulated populations of hazardous objects.
