Weak-Signal Amplification through Stochastic Resetting
Abstract
Stochastic resetting (SR) offers a distinct route to amplifying weak periodic signals in nonlinear systems. Here, we demonstrate SR-induced resonance (SRIR) in a system with double-well potential driven by a weak periodic force, where the amplitude of the mean periodic response exhibits a pronounced maximum at an optimal SR rate. The underlying mechanism exploits dynamical instability: SR repeatedly brings the system close to an unstable state, where the phase of the periodic force at each reset biases the subsequent motion toward one of the stable states. The local instability then amplifies this weak bias into a large-scale response. The SRIR arises from a competition between rare resets that limit phase-dependent state selection and frequent resets that prevent the system from fully relaxing to the selected state. We develop a general renewal formulation for the periodic response and a coarse-grained approximation that predicts the optimal resetting rate, revealing the low-frequency scaling ropt(ω)∝ω2/3, in agreement with numerical simulations. SRIR is fundamentally distinct from conventional noise-induced resonance (NIR), which relies on noise-activated interwell transitions whose rates are periodically modulated by the external driving force. In the weak-forcing and slow-driving regime, SRIR can achieve much stronger signal amplification than NIR. SRIR persists for a finite spread of resetting positions, and our framework extends beyond bistability. We further discuss how SRIR and NIR can be selectively controlled. Our work proposes a novel approach to the classical paradigm of stochastic resonance.
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BibTeX
@misc{wang2026weaksignal,
title = {{Weak-Signal Amplification through Stochastic Resetting}},
author = {Wei Wang and Kaixuan Li and Jinjie Zhu and Andrey G. Cherstvy and Ralf Metzler and Yong Xu},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.01991},
eprint = {2611.01991},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.01991},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Weak-Signal Amplification through Stochastic Resetting
AU - Wei Wang
AU - Kaixuan Li
AU - Jinjie Zhu
AU - Andrey G. Cherstvy
AU - Ralf Metzler
AU - Yong Xu
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.01991
UR - https://arxiv.org/abs/2611.01991
AB - Stochastic resetting (SR) offers a distinct route to amplifying weak periodic signals in nonlinear systems. Here, we demonstrate SR-induced resonance (SRIR) in a system with double-well potential driven by a weak periodic force, where the amplitude of the mean periodic response exhibits a pronounced maximum at an optimal SR rate. The underlying mechanism exploits dynamical instability: SR repeatedly brings the system close to an unstable state, where the phase of the periodic force at each reset biases the subsequent motion toward one of the stable states. The local instability then amplifies this weak bias into a large-scale response. The SRIR arises from a competition between rare resets that limit phase-dependent state selection and frequent resets that prevent the system from fully relaxing to the selected state. We develop a general renewal formulation for the periodic response and a coarse-grained approximation that predicts the optimal resetting rate, revealing the low-frequency scaling r_opt(ω)∝ω^2/3, in agreement with numerical simulations. SRIR is fundamentally distinct from conventional noise-induced resonance (NIR), which relies on noise-activated interwell transitions whose rates are periodically modulated by the external driving force. In the weak-forcing and slow-driving regime, SRIR can achieve much stronger signal amplification than NIR. SRIR persists for a finite spread of resetting positions, and our framework extends beyond bistability. We further discuss how SRIR and NIR can be selectively controlled. Our work proposes a novel approach to the classical paradigm of stochastic resonance.
ER -