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 AI in fundamental sciences, especially in the form of machine learning, has seen a striking increase in the past 5 years.
“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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BackgroundUnsupervised outlier detection in heavy-ion collisions
Thaprasop, P., Zhou, K., Steinheimer, J., & Herold, C. (2020) · Physica Scripta · doi:10.1088/1402-4896/abf214 · Cited by 38 · see citing works
“ML tools have nowadays become essential to face experimental challenges in high-energy physics [8–21], with applications ranging from track finding at PANDA [22] to b-jet tagging and the measurement of low-mass dielectrons at ALICE [23].”
BackgroundGPU coprocessors as a service for deep learning inference in high energy physics
Krupa, J., Lin, K., Flechas, M. A., Dinsmore, J. T., Duarte, J. M., Harris, P., Hauck, S., Holzman, B., Hsu, S.-C., Klijnsma, T., et al. (2020) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/abec21 · Cited by 38 · see citing works
“the overall quality of the data and to separate small signals from enormous backgrounds, such as the discovery of the Higgs boson [21].”
BackgroundInterpretable Geometric Deep Learning via Learnable Randomness Injection
Miao, S., Luo, Y.-N., Liu, M., & Li, P. (2022) · International Conference on Learning Representations · doi:10.48550/arxiv.2210.16966 · Cited by 37
“For example, in high energy physics (HEP), particles generated from collision experiments leave spacial signals on the detectors they pass through (Guest et al., 2018); In biology, a protein is often measured and represented as a collection of amino acids with spacial locations (Wang et al.”
BackgroundHalim, A. A., Abreu, P., Aglietta, M., Allekotte, I., Cheminant, K. A., Almela, A., Aloisio, R., Alvarez-Muñiz, J., Yebra, J. A., Anastasi, G. A., et al. (2024) · Physical Review Letters · doi:10.1103/physrevlett.134.021001 · Cited by 36
Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC
Mondal, S., & Mastrolorenzo, L. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01234-y · Cited by 36 · see citing works
“The advent of ML techniques, especially deep learning [2, 3], opened up a plethora of new possibilities of tackling the challenge of jet flavor tagging.”
Random matrix analysis of deep neural network weight matrices
Thamm, M., Staats, M., & Rosenow, B. (2022) · Physical Review E · doi:10.1103/physreve.106.054124 · Cited by 36 · see citing works
“Applications range from quantum state tomography [9, 10], locating classical [11] and quantum phase transitions [12–15], fluid turbulence [16, 17], troposphere temperature prediction [18] to classifying events in the Large Hadron Collider in search of physics beyond the standard model [19, 20].”
BackgroundUnravelling physics beyond the standard model with classical and quantum anomaly detection
Schuhmacher, J., Boggia, L., Belis, V., Puljak, E., Grossi, M., Maurizio Pierini, B., Vallecorsa, S., Tacchino, F., Barkoutsos, P. K., & Tavernelli, I. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad07f7 · Cited by 39 · see citing works
Lorentz group equivariant autoencoders
Hao, Z., Kansal, R., Duarte, J. M., & Chernyavskaya, N. (2022) · The European Physical Journal C · doi:10.1140/epjc/s10052-023-11633-5 · Cited by 35
“They have a variety of applications, ranging from classification and regression to anomaly detection and generative modeling (Guest et al., 2018; Radovic et al., 2018; Carleo et al., 2019; HEP ML Community, 2021).”
BackgroundModified empirical formulas and machine learning for α-decay systematics
Saxena, G., Sharma, P. K., & Saxena, P. (2020) · Journal of Physics G: Nuclear and Particle Physics · doi:10.1088/1361-6471/abcd1c · Cited by 35
A fast centrality-meter for heavy-ion collisions at the CBM experiment
Kuttan, M. O., Steinheimer, J., Zhou, K., Redelbach, A., & Stoecker, H. (2020) · Physics Letters B · doi:10.1016/j.physletb.2020.135872 · Cited by 35
Neural network-based top tagger with two-point energy correlations and geometry of soft emissions
Chakraborty, A., Lim, S. H., Nojiri, M., & Takeuchi, M. (2020) · Journal of High Energy Physics · doi:10.1007/jhep07(2020)111 · Cited by 35
“Interest in deep learning in collider physics [1–5] has been growing in recent years.”
Boosted decision trees in the era of new physics: a smuon analysis case study
Cornell, A., Doorsamy, W., Fuks, B., Harmsen, G., & Mason, L. (2021) · Journal of High Energy Physics · doi:10.1007/jhep04(2022)015 · Cited by 34 · see citing works
Stochastic Optimization of Contextual Neural Networks with RMSprop
Huk, M. (2020) · Asian Conference on Intelligent Information and Database Systems · doi:10.1007/978-3-030-42058-1_29 · Cited by 33 · see citing works
Energy-weighted message passing: an infra-red and collinear safe graph neural network algorithm
Konar, P., Ngairangbam, V. S., & Spannowsky, M. (2021) · Journal of High Energy Physics · doi:10.1007/jhep02(2022)060 · Cited by 32 · see citing works
Boosted Higgs boson jet reconstruction via a graph neural network
Guo, J., Li, J.-M., Li, T.-J., & Zhang, R. (2020) · Physical Review D · doi:10.1103/physrevd.103.116025 · Cited by 32 · see citing works
“[14–16] for reviews).”
Igou, T., Zhong, S., Reid, E., & Chen, Y. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.2c07578 · Cited by 31 · see citing works
“Machine learning has demonstrated predictive capability in various research areas, including physics, 38 materials science, 39 chemistry, 40 environmental engineering, 41 − 44 and many more domains.”
BackgroundTransferability of deep learning models in searches for new physics at colliders
Romão, M., Castro, N. F., Pedro, R., & Vale, T. (2019) · Physical Review D · doi:10.1103/physrevd.101.035042 · Cited by 31 · see citing works
“Although machine learning has a long history in high energy physics (HEP), we have recently witnessed a surge in interest in new methods and algorithms emerging from deep learning [1].”
MethodModeling laser-driven ion acceleration with deep learning
Djordjević, B., Kemp, A., Kim, J., Simpson, R., Wilks, S., Ma, T., & Mariscal, D. (2021) · Physics of Plasmas · doi:10.1063/5.0045449 · Cited by 30 · see citing works
Analysis and evaluation of machine learning applications in materials design and discovery
Golmohammadi, M., & Aryanpour, M. (2023) · Materials Today Communications · doi:10.1016/j.mtcomm.2023.105494 · Cited by 33 · see citing works
A cautionary tale of decorrelating theory uncertainties
Ghosh, A., & Nachman, B. (2021) · The European Physical Journal C · doi:10.1140/epjc/s10052-022-10012-w · Cited by 28 · see citing works
Ding, N., Yuan, Z., Ma, Z., Wu, Y., & Yin, L. (2024) · Molecules · doi:10.3390/molecules29153512 · Cited by 27 · see citing works
Salam, G., Wang, L.-T., & Zanderighi, G. (2022) · Nature · doi:10.1038/s41586-022-04899-4 · Cited by 27 · see citing works
“[29]) and the power of machine learning [30].”
BackgroundPerdue, G., Ghosh, A., Wospakrik, M., Akbar, F., Andrade, D. A., Ascencio, M., Bellantoni, L., Bercellie, A., Betancourt, M., Vera, G. F. R. C., et al. (2018) · Journal of Instrumentation · doi:10.1088/1748-0221/13/11/p11020 · Cited by 27
“It has made an impact at the LHC [12] and in neutrino flavor classification [13].”
BackgroundHEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency
Menzo, T., Roman, A., Gleyzer, S., Matchev, K., Fleming, G., Höche, S., Mrenna, S., & Shyamsundar, P. (2025) · doi:10.48550/arxiv.2512.15867 · Cited by 26
“…[24, 25], hadronization modeling [26–30], and fast detector simulation using generative models [31]), theory inputs such as parton distribution function determinations [32, 33], event reconstruction [34], triggering [35, 36], anomaly detection [37, 38], unfolding [39, 40], and analysis [41, 42].”
Enhancing searches for resonances with machine learning and moment decomposition
Kitouni, O., Nachman, B., Weisser, C., & Williams, M. (2020) · Journal of High Energy Physics · doi:10.1007/jhep04(2021)070 · Cited by 26 · see citing works
Wong, A. K. C., Wang, X. Y., & Hryniowski, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05835 · Cited by 16 · see citing works
“Deep learning [1] has led to the recent revolution in artificial intelligence across numerous fields, ranging from medicine [2–5] and physical sciences [6, 7], to language [8–10] and music [11], to speech [12, 13] and vision [14–17].”
BackgroundQuantum Vision Transformers for Quark–Gluon Classification
Cara, M. C., Dahale, G. R., Dong, Z., Forestano, R. T., Gleyzer, S. V., Justice, D., Kong, K., Magorsch, T., Matchev, K. T., Matcheva, K., et al. (2024) · Axioms · doi:10.3390/axioms13050323 · Cited by 18 · see citing works
Reconstructing the kinematics of deep inelastic scattering with deep learning
Arratia, M., Britzger, D., Long, O., & Nachman, B. (2021) · Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment · doi:10.1016/j.nima.2021.166164 · Cited by 25 · see citing works
Precision SMEFT bounds from the VBF Higgs at high transverse momentum
Araz, J. Y., Banerjee, S., Gupta, R. S., & Spannowsky, M. (2020) · Journal of High Energy Physics · doi:10.1007/jhep04(2021)125 · Cited by 25
A Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks
Zhang, Y., Luo, T., Ma, Z., & Xu, Z.-Q. J. (2021) · Chinese Physics Letters · doi:10.1088/0256-307x/38/3/038701 · Cited by 24 · see citing works
Revolutionizing physics: a comprehensive survey of machine learning applications
Suresh, R., Bishnoi, H., Kuklin, A. V., Parikh, A., Моlokeev, М. S., Harinarayanan, R., Gharat, S., & Hiba, P. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1322162 · Cited by 27 · see citing works
Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning
García-Ramos, J., Sáiz, Á., Arias, J. M., Lamata, L., & Pérez-Fernández, P. (2023) · Advanced Quantum Technologies · doi:10.1002/qute.202300219 · Cited by 23 · see citing works
Revealing the nature of hidden charm pentaquarks with machine learning.
Zhang, Z.-Y., Liu, J., Hu, J.-F., Wang, Q., & Meissner, U. (2023) · Science Bulletin · doi:10.1016/j.scib.2023.04.018 · Cited by 23 · see citing works
“de nuclear physics [29–35], high energy nuclear physics [36– 38], experimental data analysis [39, 40], and theoretical physics [41, 42], even to discover the physical principles underneath the experimental data [21, 43, 44].”
BackgroundLinthorne, D., & Stolarski, D. (2021) · Physical Review D · doi:10.1103/physrevd.104.035019 · Cited by 23 · see citing works
Observational Cosmology with Artificial Neural Networks
Olvera, J. D. D. R., Gómez-Vargas, I., & Vázquez, J. A. (2021) · Universe · doi:10.3390/universe8020120 · Cited by 22 · see citing works
Huerta, E. A., & Zhao, Z.-Z. (2021) · Handbook of Gravitational Wave Astronomy · doi:10.1007/978-981-15-4702-7_47-1 · Cited by 22 · see citing works
Power-law scaling to assist with key challenges in artificial intelligence
Meir, Y., Sardi, S., Hodassman, S., Kisos, K., Ben-Noam, I., Goldental, A., & Kanter, I. (2020) · Scientific Reports · doi:10.1038/s41598-020-76764-1 · Cited by 22
Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariant
Spinner, J., Favaro, L., Lippmann, P., Pitz, S., Gerhartz, G., Plehn, T., & Hamprecht, F. A. (2025) · Neural Information Processing Systems · doi:10.48550/arxiv.2505.20280 · Cited by 21
“In particular, High Energy Physics (HEP) experiments profit from ML as a vast amount of data is available [12, 22, 25].”
Leveraging universality of jet taggers through transfer learning
Dreyer, F., Grabarczyk, R., & Monni, P. F. (2022) · The European Physical Journal C · doi:10.1140/epjc/s10052-022-10469-9 · Cited by 21 · see citing works
“[39–41] for recent reviews).”
Higgs boson tagging with the Lund jet plane
Khosa, C. K., & Marzani, S. (2021) · Physical Review D · doi:10.1103/physrevd.104.055043 · Cited by 21
Invisible Higgs search through vector boson fusion: a deep learning approach
Ngairangbam, V. S., Bhardwaj, A., Konar, P., & Nayak, A. (2020) · The European Physical Journal C · doi:10.1140/epjc/s10052-020-08629-w · Cited by 20
Universal relations in composite Higgs models
Liu, D., Low, I., & Yin, Z.-W. (2018) · Journal of High Energy Physics · doi:10.1007/jhep05(2019)170 · Cited by 20
Safety of Quark/Gluon Jet Classification
Romero, A., Whiteson, D., Fenton, M., Collado, J., & Baldi, P. (2021) · doi:10.48550/arxiv.2103.09103 · Cited by 19
Self-citationDisentangling boosted Higgs Boson production modes with machine learning
Chung, Y., Hsu, S.-C., & Nachman, B. (2020) · Journal of Instrumentation · doi:10.1088/1748-0221/16/07/p07002 · Cited by 19
- 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 · 2017: 1
- 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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