where

with

The idea behind Theorem 1 is that the optimal strategy for the Brownian
motion to realise the moderate deviation event in (6) is to
behave like a Brownian motion in a drift field
for some smooth
. The cost of adopting this drift during a time
is the exponential of
times the integral in
(7). The effect of the drift is to push the Brownian
motion towards the origin. Conditioned on adopting the drift, the
Brownian motion spends time per unit volume in the
neighbourhood of , and it turns out that the Wiener
sausage covers a fraction
of the
space in that neighborhood. The best choice of the drift field is
therefore given by a minimiser of the variational problem in
(7), or by a minimising sequence.

We thus see that the optimal strategy for the Wiener sausage is to cover
only part of the space and to leave *random holes* whose sizes are
of order 1 and whose density varies on scale . This strategy is
more complicated than for (4).

Note that, apparently, a deviation *on* the scale of the mean
`does not squeeze all the empty space out of the Wiener sausage'.
Also note that the limit in (6) does depend on .

It is clear from (6) that the case is critical. The
following parallel of Theorem 1 is also proved in [1]:

**Theorem 2: ** *Let and . For every
*(9)

where is given by the same formulas as in (7-8), except that is replaced by .

Theorem 2 shows that for the moderate deviations have a polynomially small rather than an exponentially small probability. The optimal strategy is of the same type, but now the Wiener sausage lives on scale , which is only slightly below the diffusive scale. Contrary to the case , the rate function does not depend on . This means that the random holes have a typical size and a typical mutual distance that tend to infinity as , washing out the dependence on the radius of the Wiener sausage.