Counting statistics: a Feynman-Kac perspective

By building upon a Feynman-Kac formalism, we assess the distribution of the number of hits in a given region for a broad class of discrete-time random walks with scattering and absorption. We derive the evolution equation for the generating function of the number of hits, and complete our analysis by examining the moments of the distribution, and their relation to the walker equilibrium density. Some significant applications are discussed in detail: in particular, we revisit the gambler's ruin problem and generalize to random walks with absorption the arcsine law for the number of hits on the half-line.Comment: 10 pages, 6 figure

Universal properties of branching random walks in confined geometries

Characterizing the occupation statistics of a radiation flow through confined geometries is key to such technological issues as nuclear reactor design and medical diagnosis. This amounts to assessing the distribution of the travelled length $\ell$ and the number of collisions $n$ performed by the underlying stochastic transport process, for which remarkably simple Cauchy-like formulas were established in the case of branching Pearson random walks with exponentially distributed jumps. In this Letter, we show that such formulas strikingly carry over to the much broader class of branching processes with arbitrary jumps, provided that scattering is isotropic and the average jump size is finite.Comment: 5 pages, 3 figure

First-passage time of a Brownian motion: two unexpected journeys

The distribution of the first-passage time $T_a$ for a Brownian particle with drift $\mu$ subject to hitting an absorber at a level $a>0$ is well-known and given by its density $\gamma(t) = \frac{a}{\sqrt{2 \pi t^3} } e^{-\frac{(a-\mu t)^2}{2 t}}, t>0$, which is normalized only if $\mu \geq 0$. In this article, we show that there are two other families of diffusion processes (the first with one parameter and the second with two parameters) having the same first passage-time distribution when $\mu <0$. In both cases we establish the propagators and study in detail these new processes. An immediate consequence is that the distribution of the first-passage time does not allow us to know if the process comes from a drifted Brownian motion or from one of these new processes.Comment: 16 pages, 2 figures; improved mathematical notatio

Probability density function for random photon steps in a binary (isotropic-Poisson) statistical mixture

Monte Carlo (MC) simulations allowing to describe photons propagation in statistical mixtures represent an interest that goes way beyond the domain of optics, and can cover, e.g., nuclear reactor physics, image analysis or life science just to name a few. MC simulations are considered a ``gold standard'' because they give exact solutions (in the statistical sense), however, in the case of statistical mixtures they are enormously time consuming and their implementation is often extremely complex. For this reason, the aim of the present contribution is to propose a new approach that should allow us in the future to simplify the MC approach. This is done through an explanatory example, i.e.; by deriving the `exact' analytical expression for the probability density function of photons' random steps (single step function, SSF) propagating in a medium represented as a binary (isotropic-Poisson) statistical mixture. The use of the SSF reduces the problem to an `equivalent' homogeneous medium behaving exactly as the original binary statistical mixture. This will reduce hundreds time-consuming MC simulations to only one equivalent simple MC simulation. To the best of our knowledge the analytically `exact' SSF for a binary (isotropic-Poisson) statistical mixture has never been derived before