Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning
By A. Larkoski, I. Moult, Benjamin Nachman. Physics reports, 2017
507 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Nuclear and High Energy Physics
View Paper: https://www.semanticscholar.org/paper/702269ca81e00177943247c855764c764d93e193
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In Artificial Intelligence
In Nuclear and High Energy Physics
- Cms Collaboration
- Alice Collaboration
- The Cms Collaboration
- Philip C. Harris
- Benjamin Nachman
- G. Qin
- Xin-Nian Wang
- G. Panico
Citations in the corpus
- Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques (2020, background): “This understanding benefits from the theoretical progress made in jet substructure studies [3], which can result in reduced systematic uncertainties [15].”
- Machine learning and the physical sciences (2019, unknown): “(Larkoski et al., 2017) for a recent review for jet physics).”