  Physics beyond the Standard Model that is resonant in one or more dimensions
has been a longstanding focus of countless searches at colliders and beyond.
Recently, many new strategies for resonant anomaly detection have been
developed, where sideband information can be used in conjunction with modern
machine learning, in order to generate synthetic datasets representing the
Standard Model background. Until now, this approach was only able to
accommodate a relatively small number of dimensions, limiting the breadth of
the search sensitivity. Using recent innovations in point cloud generative
models, we show that this strategy can also be applied to the full phase space,
using all relevant particles for the anomaly detection. As a proof of
principle, we show that the signal from the R\&D dataset from the LHC Olympics
is findable with this method, opening up the door to future studies that
explore the interplay between depth and breadth in the representation of the
data for anomaly detection.
