2017/09/30 by Andrew J. Larkoski, Ian Moult, Benjamin Nachman · 2 citations
Physics and Astronomy · #hep-ph #hep-ex #nucl-ex #nucl-th
paper · pdf · doi:10.1016/j.physrep.2019.11.001
107 pages double spaced, 34 figures, 613 references; v2 a number of new references included, some minor rewording to the theory section, updates to machine learning section including recent advances. Now submitted to Physics Reports
arxiv created 2019/04/24 · arxiv updated 2020/01/01
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.