Optimal non-Gaussian search with stochastic resetting

In this paper we reveal that each subordinated Brownian process, leading to subdiffusion, under Poissonian resetting has a stationary state with the Laplace distribution. Its location parameter is defined only by the position to which the particle resets, and its scaling parameter is dependent on the Laplace exponent of the random process directing Brownian motion as a parent process. From the analysis of the scaling parameter the probability density function of the stochastic process, subject to reset, can be restored. In this case the mean time for the particle to reach a target is finite and has a minimum, optimal for the resetting rate. If the Brownian process is replaced by the Levy motion (superdiffusion), then its stationary state obeys the Linnik distribution which belongs to the class of generalized Laplace distributions.

Automated summary

This paper reveals that subordinated Brownian processes leading to subdiffusion under Poissonian resetting have a stationary state following the Laplace distribution. The scaling parameter depends on the Laplace exponent of the parent process directing the Brownian motion. The analysis of the scaling parameter helps in restoring the probability density function of the stochastic process subject to reset, while the mean time for the particle to reach its target is finite and can be optimized based on the resetting rate. If the Brownian process is replaced by Levy motion (superdiffusion), the stationary state follows the Linnik distribution, which belongs to the class of generalized Laplace distributions.