The multiplication of a distribution by a smooth function is . If is a Schwartz function, the Leibniz rule shows that is a continuous map , so is a tempered distribution. When both and are radial, , and
Thus Multiplication by a radial Schwartz function preserves radial tempered distributions.
For the convolution of a tempered distribution with a Schwartz function, set
Translations of depend smoothly on in the Schwartz space, so this is a smooth function with . The bound for by finitely many seminorms, together with , proves
Thus the function also defines a tempered distribution. For a radial function , put . Then , so
Hence Convolution with a radial Schwartz function preserves radial tempered distributions.
Smoothing alone need not give a Schwartz function: for the constant tempered distribution and a Schwartz function with integral one, . The radial Schwartz approximation of tempered distributions therefore combines smoothing with a large-radius cutoff. Choose a nonnegative radial mollifier , supported in the unit ball with integral one, and a radial cutoff function equal to one on the unit ball. Put
Each is a smooth function of compact support, hence a Schwartz function, and the two invariance calculations above make it radial.
It remains to prove convergence, including the simultaneous changes of both scales. With , the distributional convolution pairing is
For every fixed , the Leibniz rule, rapid decay outside the radius- ball, and the chain rule for give
Convolution by is uniformly bounded in for , because its shifts have size at most one. The mean value theorem applied to similarly gives
Splitting the error into the convolved cutoff error and the approximate identity error proves
If , this yields
Consequently
weakly, and even in the strong dual topology, since is uniformly bounded on every subset of the Schwartz space that is a bounded set in a topological vector space. No assertion that itself is rapidly decreasing is needed.