  Adjoint methods form a class of importance sampling methods that are used to
accelerate Monte Carlo (MC) simulations of transport equations. Ideally,
adjoint methods allow for zero-variance MC estimators provided that the
solution to an adjoint transport equation is known. Hybrid methods aim at (i)
approximately solving the adjoint transport equation with a deterministic
method; and (ii) use the solution to construct an unbiased MC sampling
algorithm with low variance. The problem with this approach is that both steps
can be prohibitively expensive. In this paper, we simplify steps (i) and (ii)
by calculating only parts of the adjoint solution. More specifically, in a
geometry with limited volume scattering and complicated reflection at the
boundary, we consider the situation where the adjoint solution "neglects"
volume scattering, whereby significantly reducing the degrees of freedom in
steps (i) and (ii). A main application for such a geometry is in remote sensing
of the environment using physics-based signal models. Volume scattering is then
incorporated using an analog sampling algorithm (or more precisely a simple
modification of analog sampling called a heuristic sampling algorithm) in order
to obtain unbiased estimators. In geometries with weak volume scattering (with
a domain of interest of size comparable to the transport mean free path), we
demonstrate numerically significant variance reductions and speed-ups (figures
of merit).
