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We demonstrate the possibility to systematically steer the most probableescape paths (MPEPs) by adjusting relative noise intensities in dynamicalsystems that exhibit noise-induced escape from a metastable point via a saddlepoint. Using a geometric minimum action approach, an asymptotic theory isdeveloped which is broadly applicable to fast-slow systems and shows theimportant role played by the nullcline associated with the fast variable inlocating the MPEPs. A two-dimensional quadratic system is presented whichpermits analytical determination of both the MPEPs and associated actionvalues. Analytical predictions agree with computed MPEPs, and both arenumerically confirmed by constructing prehistory distributions directly fromthe underlying stochastic differential equation.

Steering most probable escape paths by varying relative noise intensities