A multicategory jet image classification framework using deep neural network

TL;DR

This paper introduces a deep neural network framework for classifying jet images by transforming high-dimensional jet point cloud data into a separable feature space, enabling efficient and interpretable jet classification.

Contribution

The authors propose a novel feature extraction method that improves jet category separability, leading to simpler and more computationally efficient neural network models.

Findings

Comparable performance to particle flow network

High-dimensional data can be effectively classified with simpler models

Separable latent spaces facilitate efficient jet classification

Abstract

Jet point cloud images are high dimensional data structures that needs to be transformed to a separable feature space for machine learning algorithms to distinguish them with simple decision boundaries. In this article, the authors focus on jet category separability by particle and jet feature extraction, resulting in more efficient training of a simple deep neural network, resulting in a computational efficient interpretable model for jet classification. The methodology is tested with three to five categories of jets from the JetNet benchmark jet tagging dataset, resulting in comparable performance to particle flow network. This work demonstrates that high dimensional datasets represented in separable latent spaces lead to simpler architectures for jet classification.