Abstract
We investigate the run-and-tumble particle (RTP), also known as persistent Brownian motion, in one dimension. A telegraphic noise σ(t) drives the particle which changes between ±1 values at certain rates. Denoting the rate of flip from 1 to −1 as R1 and the converse rate as R2, we consider the position- and direction-dependent rates of the form and with α ⩾ 0. For α = 0 and 1, we solve the master equations exactly for arbitrary γ1 and γ2 at large t. From our analytical expression for the time-dependent probability distribution P(x, t) we find that for γ1 > γ2 the distribution relaxes to a steady state exponentially, whereas for γ1 ⩽ γ2 the distribution does not reach a steady state and can be described by a non-trivial scaling form. We interestingly find that these features of the probability distribution P(x, t) in the two regimes γ1 > γ2 and γ1 ⩽ γ2 also remain valid for general α > 0. In particular, for general α, we argue and numerically demonstrate that the approach to the steady state in γ1 > γ2 case is exponential. On the other hand, for γ1 ⩽ γ2, the distribution P(x, t) remains time dependent and possesses certain scaling behavior. For γ1 = γ2 we derive the scaling behavior as well as the scaling function rigorously, whereas for γ1 < γ2 we provide heuristic arguments to obtain the scaling behavior and the corresponding scaling functions. We also study the dynamics on a semi-infinite line with an absorbing barrier at the origin. For α = 0 and 1, we analytically compute the survival probabilities and the corresponding first-passage time distributions. For general α ⩾ 0, we provide approximate calculations to compute the behavior of the survival probability for t → ∞ in which limit it approaches a finite value for γ1 < γ2 but goes to zero for γ ⩾ γ2. We also study the approach to the large t value in both cases. Finally, we consider RTP in a finite interval [0, M] and compute the associated exit probabilities from that interval for all α. All our analytic results are verified with a numerical simulation.
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More From: Journal of Statistical Mechanics: Theory and Experiment
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