Active particle in one dimension subjected to resetting with memory
Denis Boyer, Satya N. Majumdar

TL;DR
This paper investigates a one-dimensional active particle with memory-based resetting, deriving exact position distributions, analyzing the transition from ballistic to logarithmic diffusion, and establishing a large deviation principle with unique time-scaling.
Contribution
It introduces an exact analytical framework for an active particle with memory-based resetting, combining activity and non-Markovian effects.
Findings
Derived exact Fourier space position distribution.
Identified crossover from ballistic to logarithmic diffusion.
Established large deviation principle with logarithmic time-scaling.
Abstract
The study of diffusion with preferential returns to places visited in the past has attracted an increased attention in recent years. In these highly non-Markov processes, a standard diffusive particle intermittently resets at a given rate to previously visited positions. At each reset, a position to be revisited is randomly chosen with a probability proportional to the accumulated amount of time spent by the particle at that position. These preferential revisits typically generate a very slow diffusion, logarithmic in time, but still with a Gaussian position distribution at late times. Here we consider an active version of this model, where between resets the particle is self-propelled with constant speed and switches direction in one dimension according to a telegraphic noise. Hence there are two sources of non-Markovianity in the problem. We exactly derive the position distribution in…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDiffusion and Search Dynamics · Advanced Thermodynamics and Statistical Mechanics · Micro and Nano Robotics
