  The unsupervised search for overdense regions in high-dimensional feature
spaces, where locally high population densities may be associated with
anomalous contaminations to an otherwise more uniform population, is of
relevance to applications ranging from fundamental research to industrial use
cases. Motivated by the specific needs of searches for new phenomena in
particle collisions, we propose a novel approach that targets signals of
interest populating compact regions of the feature space. The method consists
in a systematic scan of subspaces of a standardized copula of the feature
space, where the minimum p-value of a hypothesis test of local uniformity is
sought by gradient descent. We characterize the performance of the proposed
algorithm and show its effectiveness in several experimental situations.
