It is shown that one can obtain a certain kernel density estimate at a given
point 
by assuming that the sample points and are subject to the law of
universal gravitation or some variant thereof. Some distributional properties of
the resulting kernel are discussed, including the asymptotic normality of a certain
rescaled kernel. A two-stage algorithm for the selection of an optimal bandwidth
is described. The proposed density estimation technique is applied to three widely
used data sets.