Title: QCD-Aware Recursive Neural Networks for Jet Physics,
Abstract: 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.