Jet tagging via particle clouds
H. QuDepartment of Physics, University of California, Santa Barbara, California 93106, USAL. GouskosDepartment of Physics, University of California, Santa Barbara, California 93106, USA
2020en
ABI
Abstract
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.
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Cited by 80 references