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Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning

, , and . (2017)cite arxiv:1709.04464Comment: 44 pages, 34 figures, 504 references.

Abstract

Jet substructure has emerged to play a central role at the Large Hadron Collider (LHC), where it has provided numerous innovative new ways to search for new physics and to probe the Standard Model in extreme regions of phase space. In this article we provide a comprehensive review of state of the art theoretical and machine learning developments in jet substructure. This article is meant both as a pedagogical introduction, covering the key physical principles underlying the calculation of jet substructure observables, the development of new observables, and cutting edge machine learning techniques for jet substructure, as well as a comprehensive reference for experts. We hope that it will prove a useful introduction to the exciting and rapidly developing field of jet substructure at the LHC. This constitutes the theory and machine learning sections of a review on jet substructure at the LHC for Reviews of Modern Physics. An overview of recent experimental progress in jet substructure will appear separately, and the complete review will be submitted to Reviews of Modern Physics.

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Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning

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