2019/02/28 by Huilin Qu, Loukas Gouskos · 3 citations
Physics and Astronomy · Computer Science · #hep-ph #cs.CV #hep-ex
paper · pdf · doi:10.1103/physrevd.101.056019
published as Phys. Rev. D 101, 056019 (2020) · 11 pages, 4 figures; v3: updated to match the version published in PRD; Code available at https://github.com/hqucms/ParticleNet
arxiv created 2020/03/30 · arxiv updated 2020/03/31
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.