  We demonstrate the possibility to systematically steer the most probable
escape paths (MPEPs) by adjusting relative noise intensities in dynamical
systems that exhibit noise-induced escape from a metastable point via a saddle
point. Using a geometric minimum action approach, an asymptotic theory is
developed which is broadly applicable to fast-slow systems and shows the
important role played by the nullcline associated with the fast variable in
locating the MPEPs. A two-dimensional quadratic system is presented which
permits analytical determination of both the MPEPs and associated action
values. Analytical predictions agree with computed MPEPs, and both are
numerically confirmed by constructing prehistory distributions directly from
the underlying stochastic differential equation.
