Related Experiment Video
Updated: Dec 25, 2025

12:34
Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence
Published on: June 24, 2016
10.5K
Universal Survival Probability for a d-Dimensional Run-and-Tumble Particle
Francesco Mori1, Pierre Le Doussal2, Satya N Majumdar1
1LPTMS, CNRS, Université Paris-Sud, Université Paris-Saclay, 91405 Orsay, France.
Physical Review Letters
|March 24, 2020
Summary
The probability of a run-and-tumble particle
Area of Science:
- Statistical Mechanics
- Physics
- Complex Systems
Background:
- Active matter systems exhibit unique transport properties.
- Run-and-tumble particles (RTPs) are a fundamental model for active Brownian motion.
- Understanding particle trajectory statistics is crucial in various physical phenomena.
Purpose of the Study:
- To exactly compute the probability S(t) that the x-component of an RTP's position does not change sign up to time t.
- To investigate the dimensionality dependence of this probability for RTPs.
- To explore the universality of this result across different RTP models.
Main Methods:
- Exact computation of the first passage time probability.
- Application of the Sparre Andersen theorem for discrete-time random walks.
- Analysis of RTPs with constant and random tumbling rates and speeds.
Main Results:
- The probability S(t) is independent of the dimensionality (d) for finite times t when tumbling occurs at a constant rate.
- This dimensionality independence is a consequence of the Sparre Andersen theorem.
- The universality extends to RTP models with random speeds drawn from arbitrary distributions.
Conclusions:
- A universal result for the sign-change probability of RTPs is established, independent of system dimensionality and speed distribution.
- The findings highlight the robustness of statistical properties in active matter systems.
- Universality of record statistics in RTP models is demonstrated as a direct consequence.
Related Concept Videos
Collisions in Multiple Dimensions: Introduction
6.3K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
6.3K
The Uncertainty Principle
31.0K
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
31.0K
Equilibrium Conditions for a Particle
2.0K
When an object is in equilibrium, it is either at rest or moving with a constant velocity. There are two types of equilibrium: static and dynamic. Static equilibrium occurs when an object is at rest, while dynamic equilibrium occurs when an object is moving with a constant velocity. In both cases, there must be a balance of forces acting on the object.
To understand the concept of equilibrium, let us first consider the forces acting on an object. When different forces act on an object, they can...
To understand the concept of equilibrium, let us first consider the forces acting on an object. When different forces act on an object, they can...
2.0K
Probability in Statistics
21.6K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
21.6K
Probability Distributions
11.5K
The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
11.5K
Uniform Distribution
5.9K
The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
Two essential properties of this distribution are
5.9K

