  How to represent a jet is at the core of machine learning on jet physics.
Inspired by the notion of point clouds, we propose a new approach that
considers a jet as an unordered set of its constituent particles, effectively a
"particle cloud". Such a particle cloud representation of jets is efficient in
incorporating raw information of jets and also explicitly respects the
permutation symmetry. Based on the particle cloud representation, we propose
ParticleNet, a customized neural network architecture using Dynamic Graph
Convolutional Neural Network for jet tagging problems. The ParticleNet
architecture achieves state-of-the-art performance on two representative jet
tagging benchmarks and is improved significantly over existing methods.
