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Area of Science:

  • Physics
  • Statistical Mechanics
  • Complex Systems

Background:

  • Non-equilibrium systems exhibit complex dynamics due to persistent driving or interactions.
  • Stochastic resetting is a strategy to confine or accelerate processes in systems with memory effects.
  • Understanding particle diffusion in non-Markovian environments is crucial for various scientific fields.

Purpose of the Study:

  • To investigate the diffusion dynamics of a tracer particle in a non-equilibrium medium under stochastic resetting.
  • To analyze the influence of harmonic coupling, memory effects, and external forces on tracer behavior.
  • To characterize first-passage properties and search efficiency in such complex systems.

Main Methods:

  • Utilizing harmonic coupling to induce non-equilibrium conditions and memory effects in the bath.
  • Implementing a Poissonian resetting protocol that preserves the bath environment.
  • Employing a combination of numerical simulations and analytical techniques for comprehensive analysis.

Main Results:

  • The tracer's relaxation dynamics and search time are significantly affected by coupling strength and bath particle diffusivity.
  • Distinct 'hot' (high diffusivity) and 'cold' (low diffusivity) bath particles were identified.
  • Coupling to hot particles enhances the search process, while coupling to cold particles impedes it; external forces further modulate these effects.

Conclusions:

  • This research provides a framework for understanding resetting mechanisms in non-Markovian systems with memory effects.
  • The findings highlight the critical role of bath particle properties and external forces in modulating tracer dynamics.
  • Potential applications exist in active and viscoelastic media where non-equilibrium interactions and memory are significant.