# ParticleNet: Jet Tagging via Particle Clouds

## TL;DR

This paper introduces ParticleNet, a neural network architecture that treats jets as particle clouds, effectively capturing raw data and permutation symmetry, leading to state-of-the-art jet tagging performance.

## Contribution

The paper proposes a novel particle cloud representation for jets and a specialized neural network architecture, ParticleNet, which significantly improves jet tagging accuracy.

## Key findings

- Achieves state-of-the-art performance on jet tagging benchmarks.
- Effectively incorporates raw jet information and permutation symmetry.
- Significantly outperforms existing methods.

## 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.