The DOI matches its Crossref record; 32 of the 40 references it cites were found in a registry or an index.
Deep Learning and Its Application to LHC Physics
Open accessAbstract
Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.
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“The adoption of artificial intelligence (AI) in the fundamental sciences, especially in the form of machine learning (ML), has seen a striking increase in the past 5 years or so (Guest, 2018, Carleo, 2019).”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Guest, 2018
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Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes
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“For these reasons, one might anticipate deep learning to be particularly effective at the LHC. Modern deep learning is characterized by the composition of modular, differentiable components (20).”
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Cited by
Sorted by citations
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611
“The challenges here are manifold (Guest et al., 2018).”
BackgroundMachine learning and the physical sciences
Carleo, G., Cirac, J. I., Cranmer, K. S., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L. (2019) · Reviews of Modern Physics · doi:10.1103/revmodphys.91.045002 · Cited by 2,640
“(Guest et al., 2018; Radovic et al., 2018)).”
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“For the reader interested in more specialized techniques, these can be supplemented by reviews and texts on QCD relevant for jet substructure calculations [10, 11, 12, 13, 14, 15, 16, 17], as well as several standard texts and reviews on deep learning [18, 19, 20] (for particle physics [21, 22]), and the standard mach…”
MethodEnergy flow networks: deep sets for particle jets
Komiske, P., Metodiev, E. M., & Thaler, J. (2019) · Journal of High Energy Physics · doi:10.1007/jhep01(2019)121 · Cited by 302
Graph neural networks in particle physics
Shlomi, J., Battaglia, P., & Vlimant, J.-R. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abbf9a · Cited by 326
“[3] Dan Guest, Kyle Cranmer, and Daniel Whiteson.”
Background
Metadata
- Authors
- Guest, D., Cranmer, K., & Whiteson, D.
- Affiliations
- University of California, Irvine (US)
- New York University (US)
- Venue
- Annual Review of Nuclear and Particle Science
- Publisher
- Annual Reviews
- Published
- 2018-10-19
- Volume
- 68
- Issue
- 1
- Pages
- 161-181
- ISSN
- 0163-8998 (ISSN-L)
- 1545-4134
- OpenAlex
- W2810437889
- Cited by
- 417
- References
- 40
- Citations by year
- 2026: 28 · 2025: 42 · 2024: 71 · 2023: 43 · 2022: 58 · 2021: 73 · 2020: 60 · 2019: 36 · 2018: 5
- FWCI
- 3.71
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Annual Review of Nuclear and Particle Science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Particle physics theoretical and experimental studies
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Particle Detector Development and Performance
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › High-Energy Particle Collisions Research
- Keywords
- deep learning, high energy physics data analysis, Large Hadron Collider, artificial neural networks, machine learning in particle physics, LHC data analysis
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