  We investigate deterministic diffusion in periodic billiard models, in terms
of the convergence of rescaled distributions to the limiting normal
distribution required by the central limit theorem; this is stronger than the
usual requirement that the mean square displacement grow asymptotically
linearly in time. The main model studied is a chaotic Lorentz gas where the
central limit theorem has been rigorously proved. We study one-dimensional
position and displacement densities describing the time evolution of
statistical ensembles in a channel geometry, using a more refined method than
histograms. We find a pronounced oscillatory fine structure, and show that this
has its origin in the geometry of the billiard domain. This fine structure
prevents the rescaled densities from converging pointwise to gaussian
densities; however, demodulating them by the fine structure gives new densities
which seem to converge uniformly. We give an analytical estimate of the rate of
convergence of the original distributions to the limiting normal distribution,
based on the analysis of the fine structure, which agrees well with simulation
results. We show that using a Maxwellian (gaussian) distribution of velocities
in place of unit speed velocities does not affect the growth of the mean square
displacement, but changes the limiting shape of the distributions to a
non-gaussian one. Using the same methods, we give numerical evidence that a
non-chaotic polygonal channel model also obeys the central limit theorem, but
with a slower convergence rate.
