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Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning
Open accessAbstract
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.
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Cited by
Deep learning and its application to LHC physics
Guest, D., Cranmer, K., & Whiteson, D. (2018) · Annual Review of Nuclear and Particle Science · doi:10.1146/annurev-nucl-101917-021019 · Cited by 417
Chianese, M., Coogan, A., Hofma, P., Otten, S., & Weniger, C. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1477 · Cited by 36
Metadata
- Authors
- Larkoski, A. J., Moult, I., & Nachman, B.
- Venue
- Physics Reports
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 841
- Pages
- 1-63
- ISSN
- 0370-1573 (ISSN-L)
- OpenAlex
- W2756071324
- Cited by
- 361
- References
- 100
- FWCI
- 23.28
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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