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.