  We propose a new scientific application of unsupervised learning techniques
to boost our ability to search for new phenomena in data, by detecting
discrepancies between two datasets. These could be, for example, a simulated
standard-model background, and an observed dataset containing a potential
hidden signal of New Physics. We build a statistical test upon a test statistic
which measures deviations between two samples, using a Nearest Neighbors
approach to estimate the local ratio of the density of points. The test is
model-independent and non-parametric, requiring no knowledge of the shape of
the underlying distributions, and it does not bin the data, thus retaining full
information from the multidimensional feature space. As a proof-of-concept, we
apply our method to synthetic Gaussian data, and to a simulated dark matter
signal at the Large Hadron Collider. Even in the case where the background can
not be simulated accurately enough to claim discovery, the technique is a
powerful tool to identify regions of interest for further study.
