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arXiv · 2505.04489

Accelerated first-passage dynamics in a non-Markovian feedback Ornstein--Uhlenbeck process

Francesco Coghi·Romain Duvezin·John S. Wettlaufer
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Abstract

We study the first-passage dynamics of a non-Markovian stochastic process with time-averaged feedback, which we model as a one-dimensional Ornstein--Uhlenbeck process wherein the particle drift is modified by the empirical mean of its trajectory. This process maps onto a class of self-interacting diffusions. Using weak-noise large deviation theory, we calculate the leading order asymptotics of the time-dependent distribution of the particle position, derive the most probable paths that reach the specified position at a given time and quantify their likelihood via the action functional. We compute the feedback-modified Kramers rate and its inverse, which approximates the mean first-passage time, and show that the feedback accelerates dynamics by storing finite-time fluctuations, thereby lowering the effective energy barrier and shifting the optimal first-passage time from infinite to finite. Although we identify alternative mechanisms, such as slingshot and ballistic trajectories, we find that they remain sub-optimal and hence do not accelerate the dynamics. These results show how memory feedback reshapes rare event statistics, thereby offering a mechanism to potentially control first-passage dynamics.

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Francesco Coghi, Romain Duvezin, John S. Wettlaufer. 2025-05-07. Accelerated first-passage dynamics in a non-Markovian feedback Ornstein--Uhlenbeck process. https://doi.org/10.1007/s10955-025-03509-7

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