A multicategory jet image classification framework using deep neural network
This work provides a computationally efficient and interpretable model for jet classification in particle physics, though it is incremental as it builds on existing methods for feature extraction.
The authors tackled the problem of classifying high-dimensional jet point cloud images by extracting particle and jet features to create a separable latent space, enabling a simple deep neural network to achieve performance comparable to particle flow networks on the JetNet benchmark dataset.
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.