Geometrical optics of first-passage functionals of random acceleration

Random acceleration is a fundamental stochastic process encountered in many applications. In the one-dimensional version of the process a particle is randomly accelerated according to the Langevin equation $\ddot{x}(t)=\sqrt{2D}\xi (t)$, where $x(t)$ is the particle’s coordinate, $\xi (t)$ is Gaussian white noise with zero mean, and $D$ is the particle velocity diffusion constant. Here we evaluate the $A\to 0$ tail of the distribution $P_n(A\mathrm{\mid }L)$ of the functional $I[x(t)]={\int }_0^T{x}^n(t)dt=A$, where $T$ is the first-passage time of the particle from a specified point $x=L$ to the origin, and $n\ge 0$. We employ the optimal fluctuation method akin to geometrical optics. Its crucial element is determination of the optimal path – the most probable realization of the random acceleration process $x(t)$, conditioned on specified $A\to 0$, $n$ and $L$. This realization dominates the probability distribution $P_n(A\mathrm{\mid }L)$. We show that the $A\to 0$ tail of this distribution has a universal essential singularity, $P_n(A\to 0\mathrm{\mid }L)\sim \exp (-\frac{{\alpha }_n{L}^{3n+2}}{D{A}^3})$, where ${\alpha }_n$ is an $n$-dependent number which we calculate analytically for $n=0,1$ and $2$ and numerically for other $n$. For $n=0$ our result agrees with the asymptotic of the previously found first-passage time distribution.