  We investigate the performance of a jet identification algorithm based on
interaction networks (JEDI-net) to identify all-hadronic decays of
high-momentum heavy particles produced at the LHC and distinguish them from
ordinary jets originating from the hadronization of quarks and gluons. The jet
dynamics are described as a set of one-to-one interactions between the jet
constituents. Based on a representation learned from these interactions, the
jet is associated to one of the considered categories. Unlike other
architectures, the JEDI-net models achieve their performance without special
handling of the sparse input jet representation, extensive pre-processing,
particle ordering, or specific assumptions regarding the underlying detector
geometry. The presented models give better results with less model parameters,
offering interesting prospects for LHC applications.
