English

First-passage Brownian functionals with stochastic resetting

Statistical Mechanics 2022-06-08 v1

Abstract

We study the statistical properties of first-passage time functionals of a one dimensional Brownian motion in the presence of stochastic resetting. A first-passage functional is defined as V=0tfZ[x(τ)]V=\int_0^{t_f} Z[x(\tau)] where tft_f is the first-passage time of a reset Brownian process x(τ)x(\tau), i.e., the first time the process crosses zero. In here, the particle is reset to xR>0x_R>0 at a constant rate rr starting from x0>0x_0>0 and we focus on the following functionals: (i) local time Tloc=0tfdτ δ(xxR)T_{loc} = \int _0^{t_f}d \tau ~ \delta (x-x_R), (ii) residence time Tres=0tfdτ θ(xxR)T_{res} = \int _0^{t_f} d \tau ~\theta (x-x_R), and (iii) functionals of the form An=0tfdτ[x(τ)]nA_n = \int _{0}^{t_f} d \tau [x(\tau)]^n with n>2n >-2. For first two functionals, we analytically derive the exact expressions for the moments and distributions. Interestingly, the residence time moments reach minima at some optimal resetting rates. A similar phenomena is also observed for the moments of the functional AnA_n. Finally, we show that the distribution of AnA_n for large AnA_n decays exponentially as exp(An/an)\sim \text{exp}\left( -A_n/a_n\right) for all values of nn and the corresponding decay length ana_n is also estimated. In particular, exact distribution for the first passage time under resetting (which corresponds to the n=0n=0 case) is derived and shown to be exponential at large time limit in accordance with the generic observation. This behavioural drift from the underlying process can be understood as a ramification due to the resetting mechanism which curtails the undesired long Brownian first passage trajectories and leads to an accelerated completion. We confirm our results to high precision by numerical simulations.

Keywords

Cite

@article{arxiv.2202.02715,
  title  = {First-passage Brownian functionals with stochastic resetting},
  author = {Prashant Singh and Arnab Pal},
  journal= {arXiv preprint arXiv:2202.02715},
  year   = {2022}
}

Comments

26 pages, 10 figures

R2 v1 2026-06-24T09:22:19.630Z