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.

该文章研究了通过粒子和喷流特征提取将喷流类别分离到可分离特征空间中，从而使简单的深度神经网络能够更有效地训练，获得计算高效且可解释的喷流分类模型，并在JetNet基准测试中对三到五类喷流进行了验证，表现与粒子流网络相当。这项工作表明，用可分离的潜在空间表示高维数据集可导致喷流分类的简化体系结构。

多类别喷射图像分类框架使用深度神经网络