  We propose and analyze a reliable and efficient a posteriori error estimator
for a constrained linear-quadratic optimal control problem involving Dirac
measures; the control variable corresponds to the amplitude of forces modeled
as point sources. The proposed a posteriori error estimator is defined as the
sum of two contributions, which come from the state and adjoint equations. The
estimator associated with the state equation is based on Muckenhoupt weighted
Sobolev spaces, while the one associated with the adjoint is in the maximum
norm and allows for unbounded right hand sides. The analysis is valid for two
and three-dimensional domains. On the basis of the devised a posteriori error
estimator, we design a simple adaptive strategy that yields optimal rates of
convergence for the numerical examples that we perform.
