First-passage Brownian functionals with stochastic resetting

被引:31
作者
Singh, Prashant [1 ]
Pal, Arnab [2 ,3 ]
机构
[1] Tata Inst Fundamental Res, Int Ctr Theoret Sci, Bengaluru 560089, India
[2] Inst Math Sci, CIT Campus, Chennai 600113, Tamil Nadu, India
[3] Homi Bhabha Natl Inst, Training Sch Complex, Mumbai 400094, Maharashtra, India
关键词
stochastic resetting; first passage time; Brownian functionals; LOCAL TIME; JOINT DISTRIBUTION; AREA; MODEL;
D O I
10.1088/1751-8121/ac677c
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
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 = integral(tf)(0) Z[x(tau)] where t(f) is the first-passage time of a reset Brownian process x(tau), i.e., the first time the process crosses zero. In here, the particle is reset to x(R) > 0 at a constant rate r starting from x(0) > 0 and we focus on the following functionals: (i) local time T-loc = integral(tf)(0) d tau delta(x - x(R)), (ii) residence time T-res = integral(tf)(0) d tau theta(x - x(R)), and (iii) functionals of the form A(n) = integral(tf)(0) d tau[x(tau)](n) with n > -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 A(n). Finally, we show that the distribution of A(n) for large A(n) decays exponentially as similar to exp(-A(n)/a) for all values of n and the corresponding decay length a(n) is also estimated. In particular, exact distribution for the first passage time under resetting (which corresponds to the n = 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.
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页数:25
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