  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.
