QCD-aware recursive neural networks for jet physics

Gilles Louppe, Kyunghyun Cho, Cyril Becot, Kyle Cranmer

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number57
JournalJournal of High Energy Physics
Volume2019
Issue number1
DOIs
StatePublished - Jan 1 2019

Keywords

  • Jets
  • QCD Phenomenology

ASJC Scopus subject areas

  • Nuclear and High Energy Physics

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