  We consider the problem of estimating the density $\Pi$ of a determinantal
process $N$ from the observation of $n$ independent copies of it. We use an
aggregation procedure based on robust testing to build our estimator. We
establish non-asymptotic risk bounds with respect to the Hellinger loss and
deduce, when $n$ goes to infinity, uniform rates of convergence over classes of
densities $\Pi$ of interest.
