  Model-independent searches in particle physics aim at completing our
knowledge of the universe by looking for new possible particles not predicted
by the current theories. Such particles, referred to as signal, are expected to
behave as a deviation from the background, representing the known physics.
Information available on the background can be incorporated in the search, in
order to identify potential anomalies. From a statistical perspective, the
problem is recasted to a peculiar classification one where only partial
information is accessible. Therefore a semisupervised approach shall be
adopted, either by strengthening or by relaxing assumptions underlying
clustering or classification methods respectively. In this work, following the
first route, we semisupervise nonparametric clustering in order to identify a
possible signal. The main contribution consists in tuning a nonparametric
estimate of the density underlying the experimental data with the aid of the
available information on the physical theory. As a side contribution, a
variable selection procedure is presented. The whole procedure is tested on a
dataset mimicking proton-proton collisions performed within a particle
accelerator. While finding motivation in the field of particle physics, the
approach is applicable to various science domains, where similar problems of
anomaly detection arise.
