Recent progress in applying machine learning for jet physics has been built
upon an analogy between calorimeters and images. In this work, we present a
novel class of recursive neural networks built instead upon an analogy between
QCD and natural languages. In the analogy, four-momenta are like words and the
clustering history of sequential recombination jet algorithms is like the
parsing of a sentence. Our approach works directly with the four-momenta of a
variable-length set of particles, and the jet-based tree structure varies on an
event-by-event basis. Our experiments highlight the flexibility of our method
for building task-specific jet embeddings and show that recursive architectures
are significantly more accurate and data efficient than previous image-based
networks. We extend the analogy from individual jets (sentences) to full events
(paragraphs), and show for the first time an event-level classifier operating
on all the stable particles produced in an LHC event.

本文介绍了一种基于量子色动力学和自然语言的类递归神经网络，该方法在从可变长度集合的粒子四动量中建立任务特定的喷注嵌入方面具有灵活性，在所有稳定粒子上操作的事件级分类器首次展示了类比从单个喷注（句子）扩展至完整事件（段落）的能力。