  With Wendelstein 7-X now up and running, and the construction of ITER
proceeding, predicting fast-ion losses to sensitive plasma-facing components
and detectors is gaining significant interest. A common recipe to perform such
studies is to push a large population of marker particles along their equations
of motion, the trajectories randomized with Monte Carlo operators accounting
for Coulomb collisions, and to record possible intersections of the marker
trajectories with synthetic detectors or areas of interest in the first wall.
While straightforward to implement and easy to parallelize, this Forward Monte
Carlo (FMC) approach tends to suffer from poor statistics and error estimation
as the detector domain is often small: it is difficult to guess how to set up
the initial weights and locations of the markers for them to remain
representative of the source distribution, yet record enough hits to the
detector for good statistics. As an alternative, the FMC method can be replaced
with a so-called Backward Monte Carlo (BMC) algorithm. Instead of starting with
a given initial marker population, one starts from the end condition at the
detector and records how the hit probability evolves backwards in time. The
scheme eliminates the statistics issue present in the FMC scheme and may
provide more accurate and efficient simulations of fast-ion loss signals. The
purpose of this paper is to explain the BMC recipe in the fast-ion setting and
to discuss the associated nuances, especially how to negate artificial
diffusion. For illustration purposes, our numerical example considers a 1-D
stochastic harmonic oscillator as a mock-up of a charged particle.
