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arXiv · 1902.08570

ParticleNet: Jet Tagging via Particle Clouds

Huilin Qu·Loukas Gouskos
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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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Huilin Qu, Loukas Gouskos. 2020-03-30. ParticleNet: Jet Tagging via Particle Clouds. https://doi.org/10.1103/physrevd.101.056019

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