Cited by 417Particle physics theoretical and experimental studiesParticle Detector Development and PerformanceHigh-Energy Particle Collisions Research

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Deep Learning and Its Application to LHC Physics

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Guest, Dan, Cranmer, Kyle, Whiteson, Daniel·2018·Annual Review of Nuclear and Particle Science

DOI 10.1146/annurev-nucl-101917-021019

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Abstract

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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“[8] Dan Guest, Kyle Cranmer, and Daniel Whiteson, “Deep Learning and its Application to LHC Physics,” Ann.”
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Deep Learning Based Impact Parameter Determination for the CBM Experiment
Kuttan, M. O., Steinheimer, J., Zhou, K., Redelbach, A., & Horst Stöcker, B. (2021) · Particles · doi:10.3390/particles4010006 · Cited by 17
Development of a vertex finding algorithm using Recurrent Neural Network
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The neutron star outer crust equation of state: a machine learning approach
Anil, M. U., Banerjee, K., Malik, T., & Providência, C. (2022) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2022/01/045 · Cited by 17
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Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery
Bachhofner, S., Loghin, A.-M., Otepka, J., Pfeifer, N., Hornáček, M., Siposova, A., Schmidinger, N., Hornik, K., Schiller, N., Kähler, O., et al. (2020) · Remote Sensing · doi:10.3390/rs12081289 · Cited by 14
A tagger for strange jets based on tracking information using long short-term memory
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Supporting High-Performance and High-Throughput Computing for Experimental Science
Huerta, E. A., Haas, R., Jha, S., Neubauer, M., & Katz, D. (2018) · Computing and Software for Big Science · doi:10.1007/s41781-019-0022-7 · Cited by 12
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Imaging particle collision data for event classification using machine learning
Chekanov, S. (2018) · Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment · doi:10.1016/j.nima.2019.04.031 · Cited by 12
Artificial Intelligence and Machine Learning in Nuclear Physics
Boehnlein, A., Diefenthaler, M., Fanelli, C., Hjorth-Jensen, M., Horn, T., Kuchera, M., Lee, D., Nazarewicz, W., Orginos, K., Ostroumov, P., et al. (2021) · arXiv.org · doi:10.48550/arxiv.2112.02309 · Cited by 12
“In addition, the selection of the correct seed results in improved tracking eﬃciency (Gavalian et al. , 2020; Guest et al. , 2018).”
Background
Optimal equivariant architectures from the symmetries of matrix-element likelihoods
Maître, D., Ngairangbam, V. S., & Spannowsky, M. (2024) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/adbab1 · Cited by 11
Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach
Gavrikov, A., Malyshkin, Y., & Ratnikov, F. (2022) · The European Physical Journal C · doi:10.1140/epjc/s10052-022-11004-6 · Cited by 11
Boundary between noise and information applied to filtering neural network weight matrices
Staats, M., Thamm, M., & Rosenow, B. (2023) · Physical review. E · doi:10.1103/physreve.108.l022302 · Cited by 11
“In recent years, deep neural networks (DNNs) have proven to be powerful tools for solving a wide range of problems [1–5], including a large number of applications in physics [6–16].”
Deep learning jet images as a probe of light Higgsino dark matter at the LHC
Lv, H., Wang, D., & Wu, L. (2022) · Physical Review D · doi:10.1103/physrevd.106.055008 · Cited by 11
Evaluating the faithfulness of PDF uncertainties in the presence of inconsistent data
Barontini, A., Costantini, M. N., Crescenzo, G. D., Forte, S., & Ubiali, M. (2025) · doi:10.48550/arxiv.2503.17447 · Cited by 10
Of opaque oracles: epistemic dependence on AI in science poses no novel problems for social epistemology
Ortmann, J. (2025) · Synthese · doi:10.1007/s11229-025-04930-x · Cited by 10
“First, in high-energy physics, DNN systems are used to pre-filter the massive amounts of data generated by experiments (Guest et al., 2018).”
Hyperparameter optimisation in deep learning from ensemble methods: applications to proton structure
Cruz-Martinez, J., Jansen, A., van Oord, G., Rabemananjara, T., Rocha, C. M. R., Rojo, J., & Stegeman, R. (2024) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/adcd39 · Cited by 10
“A more extensive overview of ML applications to particle physics can be found in the reviews of [15–19] and references therein.”
Search for single vector-like B quark production in hadronic final states at the LHC
Yang, B.-F., Li, Z.-J., Jia, X., Moretti, S., & Shang, L. (2024) · The European Physical Journal C · doi:10.1140/epjc/s10052-024-13482-2 · Cited by 10
“…data analysis techniques use a sequence of boolean decisions followed by statistical analysis on the selected data, both of which are based on the distribution of a single observed quantity motivated by physics considerations, which is not easily extended to the required higher dimensions [49].”
Method
LHC study of third-generation scalar leptoquarks with machine-learned likelihoods
Arganda, E., Díaz, D. A., Perez, A. D., Seoane, R. M. S., & Szynkman, A. (2023) · Physical Review D · doi:10.1103/physrevd.109.055032 · Cited by 10
Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicists
Fioresi, R., & Zanchetta, F. (2023) · International Journal of Geometric Methods in Modern Physics (IJGMMP) · doi:10.48550/arxiv.2305.05601 · Cited by 10
“Recently at Cern the power of deep learning was employed for the analysis of LHC (Large Hadron Collider) data [16]; particle physics is more and more being investigated by these new and powerful methods (see the review [12] and refs.”
Method
Lorenzetti Showers - A general-purpose framework for supporting signal reconstruction and triggering with calorimeters
Araújo, M., Begalli, M., Freund, W., Gonçalves, G. I., Khandoga, M., Laforge, B., Leopold, A., Marin, J., Peralva, B., Pinto, J. V. F., et al. (2023) · Computer Physics Communications · doi:10.1016/j.cpc.2023.108671 · Cited by 10
TeV-scale lepton number violation: Connecting leptogenesis, neutrinoless double beta decay, and colliders
Harz, J., Ramsey-Musolf, M., Shen, T., & Urrutia-Quiroga, S. (2021) · Physical Review D · doi:10.1103/physrevd.110.035024 · Cited by 10
Towards a method to anticipate dark matter signals with deep learning at the LHC
Arganda, E., Medina, A., Perez, A. D., & Szynkman, A. (2021) · SciPost Physics · doi:10.21468/scipost.report.3880 · Cited by 10
A Review and Perspective on the Main Machine Learning Methods Applied to Physical Sciences
Méndez-Gurrola, I. I., Ramírez-Reyes, A., & Gutiérrez, A. I. B. (2022) · Acta Polytechnica Hungarica · doi:10.12700/aph.19.10.2022.10.12 · Cited by 10
Unsupervised learning of interacting topological phases from experimental observables
Yu, L.-W., Zhang, S., Shen, P.-X., & Deng, D.-L. (2023) · Fundamental Research · doi:10.1016/j.fmre.2022.12.016 · Cited by 9
“…learning techniques are powerful in identifying hidden patterns of complex data, and have recently been applied in various contexts of quantum physics [34,35] , ranging from high energy physics [36] , quantum dynamics [37] , quantum technologies [38–41] , to condensed matter physics [42–44] .”
Using machine learning to improve neutron identification in water Cherenkov detectors
Jamieson, B., Stubbs, M., Ramanna, S., Walker, J., Prouse, N., Akutsu, R., de Perio, P., & Fedorko, W. (2022) · Frontiers in Big Data · doi:10.3389/fdata.2022.978857 · Cited by 9
“[8] describes the use of an assortment of machine learning techniques for MVA in the physics context, include support vector machines, kernel density estimation, random forests, boosted decision trees, etc.”
Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC
Orzari, B., Tomei, T., Pierini, M., Touranakou, M., Duarte, J. M., Kansal, R., Vlimant, J., & Gunopulos, D. (2021) · LatinX in AI at International Conference on Machine Learning 2021 · doi:10.52591/202107246 · Cited by 9
“physics (Guest et al., 2018; Albertsson et al., 2018), the usage of ML to build a generator of events can be seen as the next step for the current Monte Carlo based generators.”
Background
Deep Learning Image Feature Recognition Algorithm for Judgment on the Rationality of Landscape Planning and Design
Hu, B. (2021) · Complexity · doi:10.1155/2021/9921095 · Cited by 8
“There are several convolution kernels to get several output matrices [18]. output data will eventually become the extracted landscape feature value. pooling layer can perform down-sampling operations on the input landscape pattern data. purpose is to remove excess information, improve training…”
Background
Class imbalance techniques for high energy physics
Murphy, C. (2019) · SciPost Physics · doi:10.21468/scipostphys.7.6.076 · Cited by 9
“[4, 5, 6, 7] for some recent reviews.”
Multi-scale Optimal Transport for Complete Collider Events
Cai, T.-J., Craig, N., Craig, K., & Lin, X. (2025) · doi:10.48550/arxiv.2501.10681 · Cited by 8
Streamlining latent spaces in machine learning using moment pooling
Gambhir, R., Osathapan, A., & Thaler, J. (2024) · Physical Review D · doi:10.1103/physrevd.110.074020 · Cited by 8
KAN We Improve on HEP Classification Tasks? Kolmogorov–Arnold Networks Applied to an LHC Physics Example
Erdmann, J., Mausolf, F., & Spah, J. L. (2024) · Computing and Software for Big Science · doi:10.1007/s41781-025-00138-3 · Cited by 8
Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service
Cms Collaboration (2024) · Computing and Software for Big Science · doi:10.1007/s41781-024-00124-1 · Cited by 8
“Within high-energy physics (HEP), deep-learning (DL) algorithms are already widely used for regression and clas-siﬁcation tasks, and their popularity is growing rapidly [35– 39].”
Background
From optimal observables to machine learning: an effective-field-theory analysis of e^+e^− → W^+W^− at future lepton colliders
Chai, S.-D., Gu, J.-Y., & Li, L.-F. (2024) · Journal of High Energy Physics · doi:10.1007/jhep05(2024)292 · Cited by 8
LLPNet: Graph Autoencoder for Triggering Light Long-Lived Particles at HL-LHC
Bhattacherjee, B., Konar, P., Ngairangbam, V. S., & Solanki, P. (2023) · doi:10.48550/arxiv.2308.13611 · Cited by 8
Reconstructing axion-like particles from beam dumps with simulation-based inference
Morandini, A., Ferber, T., & Kahlhoefer, F. (2023) · The European Physical Journal C · doi:10.1140/epjc/s10052-024-12557-4 · Cited by 8
“Of particular importance for us is the application of ML to LHC physics [6,7] and to searches for new physics [8].”
Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors
Knipfer, M., Meier, S., Volk, T., Heimerl, J., Hommelhoff, P., & Gleyzer, S. V. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad3d2d · Cited by 7
Invariance-Aware Randomized Smoothing Certificates
Schuchardt, J., & Günnemann, S. (2022) · Neural Information Processing Systems · doi:10.48550/arxiv.2211.14207 · Cited by 8
“[43] Dan Guest, Kyle Cranmer, and Daniel Whiteson.”
Probing Higgs boson exotic decays at the LHC with machine learning
Jung, S., Liu, Z., Wang, L.-T., & Xie, K.-P. (2021) · Physical Review D · doi:10.1103/physrevd.105.035008 · Cited by 8
Characterizing the acceleration time of laser-driven ion acceleration with data-informed neural networks
Djordjevic, B. Z., Kemp, A. J., Kim, J., Ludwig, J., Simpson, R. A., Wilks, S. C., Ma, T., & Mariscal, D. A. (2021) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/ac172a · Cited by 10
Learning to increase matching efficiency in identifying additional b-jets in the $$\text {t}\bar{\text {t}}\text {b}\bar{\text {b}}$$ process
Jang, C., Ko, S.-K., Choi, J., Lim, J., Noh, Y., & Kim, T. J. (2022) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-022-03024-8 · Cited by 5
A Survey of Interpretability of Machine Learning in Accelerator-based High Energy Physics
Turvill, D., Barnby, L. S., Yuan, B., & Zahir, A. (2020) · IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) · doi:10.1109/bdcat50828.2020.00025 · Cited by 9
“The difficulty of achieving interpretability is mentioned in [40] as a general domain-wide issue.”
Background
PulseDL: A reconfigurable deep learning array processor dedicated to pulse characterization for high energy physics detectors
Ai, P., Wang, D., Hu, J. F., Shen, F., Fang, N., Xu, D., Wang, H., & Chen, J. (2020) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2020.164420 · Cited by 10
Beyond $$M_{t\bar{t}}$$: learning to search for a broad $$t\bar{t}$$ resonance at the LHC
Jung, S., Lee, D., & Xie, K.-P. (2019) · The European Physical Journal C · doi:10.1140/epjc/s10052-020-7672-9 · Cited by 8
Perspective on machine learning for real-time analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
Astrand, S., Boggia, L., Borsato, M., Bozianu, L., Toapaxi, C. C., Giasemis, F. I., Hansen, J., Inkaew, P., Iversen, K. E., Jawahar, P., et al. (2026) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ae35cc · Cited by 13
“Though this problem is an open area of study, the large LHC experiments employ several strategies to mitigate it, including: validation using adjacent con-trol regions, weakly supervised learning using real data and parameterising known nuisance parameters within the network architecture [59].”
Quantum Chebyshev probabilistic models for fragmentation functions
de Lejarza, J. J. M., Wu, H., Kyriienko, O., Rodrigo, G., & Grossi, M. (2025) · Communications Physics · doi:10.1038/s42005-025-02361-1 · Cited by 7
Mixture-of-experts graph transformers for interpretable particle collision detection
Genovese, D., Sgroi, A., Devoto, A., Valentine, S., Wood, L., Sebastiani, C., Scardapane, S., D'Onofrio, M., & Giagu, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-12003-9 · Cited by 7
“Machine learning (ML) and deep learning techniques have demonstrated strong potential in tackling HEP challenges [4], including particle identification [5], event reconstruction [6], and background subtraction [7].”
Paths towards time evolution with larger neural-network quantum states
Zhang, W., Xing, B., Xu, X., & Poletti, D. (2025) · Computer Physics Communications · doi:10.1016/j.cpc.2025.109577 · Cited by 5
Classifier surrogates: sharing AI-based searches with the world
Bieringer, S., Kasieczka, G., Kieseler, J., & Trabs, M. (2024) · The European Physical Journal C · doi:10.1140/epjc/s10052-024-13353-w · Cited by 7
“Current experimental work in particle physics, for example by the ATLAS and CMS collaborations, uses deep-learning based taggers to great success [1–4].”
Artificial intelligence for improved fitting of trajectories of elementary particles in dense materials immersed in a magnetic field
Monsalve, S. A., Sgalaberna, D., Zhao, X., McGrew, C., & Rubbia, A. (2023) · Communications Physics · doi:10.1038/s42005-023-01239-4 · Cited by 6
Realizing the potential of deep neural network for analyzing neutron star observables and dense matter equation of state
Thete, A., Banerjee, K., & Malik, T. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.108.063028 · Cited by 18
Data Science and Machine Learning in Education
Benelli, G., Chen, T. Y., Duarte, J. M., Feickert, M., Graham, M., Gray, L., Hackett, D., Harris, P. C., Hsu, S.-C., Kasieczka, G., et al. (2022) · arXiv.org · doi:10.48550/arxiv.2207.09060 · Cited by 7
Learning Latent Jet Structure
Dillon, B., Faroughy, D., Kamenik, J., & Szewc, M. (2021) · Symmetry · doi:10.3390/sym13071167 · Cited by 7
Deep neural networks to recover unknown physical parameters from oscillating time series
Garcon, A., Vexler, J., Budker, D., & Kramer, S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0268439 · Cited by 7
“started to be used in the field only in recent years [3].”
Calculating pull for non-singlet jets
Bao, Y., & Larkoski, A. (2019) · Journal of High Energy Physics · doi:10.1007/jhep12(2019)035 · Cited by 7
“Observables that are more sensitive to color flow between jets can be designed, motivated by recent work in machine learning for particle physics [28–30].”
Background
Salt: Multimodal Multitask Machine Learning for High Energy Physics
Barr, J., Biswas, D., Draguet, M., Gadow, P., Haines, E., Karkout, O., Kobylianskii, D., Lai, W.-S., Leigh, M., Luongo, N., et al. (2025) · Journal of Open Source Software · doi:10.21105/joss.07217 · Cited by 6
“In high energy physics research the reliance on ML for data analysis and object classification is growing (Cagnotta et al., 2022; Guest et al., 2018).”
Physics-informed neural network approach to quark-antiquark color flux tube
Kou, W., Lin, X.-X., Guo, B., & Chen, X.-R. (2025) · Physical Review D · doi:10.1103/z61n-r7gw · Cited by 6
Machine learning insights into quark–antiquark interactions: probing field distributions and string tension in QCD
Kou, W., & Chen, X. (2024) · The European Physical Journal C · doi:10.1140/epjc/s10052-025-13958-9 · Cited by 6
Systematic Interpretability and the Likelihood for Boosted Top Quark Identification
Larkoski, A. (2024) · doi:10.48550/arxiv.2411.00104 · Cited by 6
Meson mass and width: Deep learning approach
Malekhosseini, M., Rostami, S., Olamaei, A., Ostovar, R., & Azizi, K. (2024) · Physical Review D · doi:10.1103/physrevd.110.054011 · Cited by 6
“Application of the NNs to the HEP experiments and phenomenology has been very successful [41, 70–80].”
Method
Discrimination of pp solar neutrinos and 14C double pile-up events in a large-scale LS detector
Chen, G.-M., Zhang, X., Yu, Z.-Z., Zhang, S.-Q., Xu, Y., Wu, W.-J., Wang, Y.-M., & Huang, Y.-B. (2023) · Nuclear Science and Techniques · doi:10.1007/s41365-023-01295-z · Cited by 6

Authors: Guest, D., Cranmer, K., & Whiteson, D.
Affiliations: University of California, Irvine (US)
New York University (US)
ORCID: Guest, Dan
Cranmer, Kyle
Whiteson, Daniel
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
DOI: 10.1146/annurev-nucl-101917-021019
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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Deep Learning and Its Application to LHC Physics

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