The first-passage time (FPT), defined as the time a random walker takes to
reach a target point in a confining domain, is a key quantity in the theory of
stochastic processes. Its importance comes from its crucial role to quantify
the efficiency of processes as varied as diffusion-limited reactions, target
search processes or spreading of diseases. Most methods to determine the FPT
properties in confined domains have been limited to Markovian (memoryless)
processes. However, as soon as the random walker interacts with its
environment, memory effects can not be neglected. Examples of non Markovian
dynamics include single-file diffusion in narrow channels or the motion of a
tracer particle either attached to a polymeric chain or diffusing in simple or
complex fluids such as nematics \cite{turiv2013effect}, dense soft colloids or
viscoelastic solution. Here, we introduce an analytical approach to calculate,
in the limit of a large confining volume, the mean FPT of a Gaussian
non-Markovian random walker to a target point. The non-Markovian features of
the dynamics are encompassed by determining the statistical properties of the
trajectory of the random walker in the future of the first-passage event, which
are shown to govern the FPT kinetics.This analysis is applicable to a broad
range of stochastic processes, possibly correlated at long-times. Our
theoretical predictions are confirmed by numerical simulations for several
examples of non-Markovian processes including the emblematic case of the
Fractional Brownian Motion in one or higher dimensions. These results show, on
the basis of Gaussian processes, the importance of memory effects in
first-passage statistics of non-Markovian random walkers in confinement.Comment: Submitted version. Supplementary Information can be found on the
Nature website :
http://www.nature.com/nature/journal/v534/n7607/full/nature18272.htm