  Deciphering the complex information contained in jets produced in collider
events requires a physical organization of the jet data. We introduce
two-particle correlations (2PCs) by pairing individual particles as the initial
jet representation from which a probabilistic model can be built. Particle
momenta, as well as particle types and vertex information are included in the
correlation. A novel, two-particle correlation neural network (2PCNN)
architecture is constructed by combining neural network based filters on 2PCs
and a deep neural network for capturing jet kinematic information. The 2PCNN is
applied to boosted boson and heavy flavor tagging, and it achieves excellent
performance by comparing to models based on telescoping deconstruction. Major
correlation pairs exploited in the trained models are also identified, which
shed light on the physical significance of certain jet substructure.
