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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Items 201–300 of 389

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“In terms of fat-jet tagging efficiency, machine-learning techniques have proven to substantially outperform those jet substructure techniques [11, 19–22].”
Method
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Alhazmi, H., Dong, Z., Huang, L., Kim, J. H., Kong, K., & Shih, D. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.105.115011 · Cited by 9
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BackgroundMethod
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Method
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Machine learning approach for the search of resonances with topological features at the Large Hadron Collider
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Background
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Background
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Influential
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Lu, J., Liu, S., Kobylianskii, D., Dreyer, E., Gross, E., & Liang, S. (2024) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/ad8060 · Cited by 4
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Franceschini, R., Kim, D., Kong, K., Matchev, K., Park, M., & Shyamsundar, P. (2023) · Reviews of Modern Physics · doi:10.1103/revmodphys.95.045004 · Cited by 4
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Simulation and Optimization Studies of the LHCb Beetle Readout ASIC and Machine Learning Approach for Pulse Shape Reconstruction
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Method
Deep neural network application: Higgs boson CP state mixing angle in H→ττ decay and at the LHC
Lasocha, K., Richter-Was, E., Sadowski, M., & Was, Z. (2021) · Physical Review D · doi:10.1103/physrevd.103.036003 · Cited by 4
Inferring hidden symmetries of exotic magnets from detecting explicit order parameters
Rao, N., Liu, K., & Pollet, L. (2021) · Physical review. E · doi:10.1103/physreve.104.015311 · Cited by 5
High performance FPGA embedded system for machine learning based tracking and trigger in sPhenix and EIC
Xuan, T., Durao, F., & Sun, Y. (2022) · doi:10.1088/1748-0221/17/07/c07003 · Cited by 4
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Method
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Zhang, X., Huang, H., Zhang, D., Zhuang, S., Han, S., Liu, H., & Lai, P. (2026) · Advanced Photonics · doi:10.1117/1.ap.8.3.036015 · Cited by 3
Analysis of single event induced bit faults in a deep neural network accelerator pipeline
Jonckers, N., Vinck, T., Karsmakers, P., & Prinzie, J. (2025) · Journal of Instrumentation · doi:10.1088/1748-0221/21/02/c02021 · Cited by 3
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Jet Reconstruction with Mamba Networks in Collider Events
Li, J., Li, P., Long, B., & Zhang, R. (2025) · doi:10.1103/pwp8-ffqk · Cited by 3
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FPGA Implementation of a CNN-Based Topological Trigger for HL-LHC
Brooke, J., Clement, E., Glowacki, M., Paramesvaran, S., & Segal, J. (2025) · Computing and Software for Big Science · doi:10.1007/s41781-025-00150-7 · Cited by 3
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Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
Arganda, E., Carena, M., Rios, M. D. L., Perez, A. D., Rocha, D., Seoane, R. M. S., & Wagner, C. E. M. (2024) · Journal of High Energy Physics · doi:10.1007/jhep07(2025)014 · Cited by 3
Discriminating sub-TeV gamma and hadron-induced showers through their footprints
Conceição, R., González, B. S., Guillén, A., Pimenta, M. J. M., & Tome, B. (2025) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.111.043047 · Cited by 4
“…such as the IceCube detection of neutrinos from the galactic plane [30], investigations on the mass composition and X max estimation of ultra-high-energy cosmic rays (UHECRs) by the Pierre Auger Observatory [31– 33], and the search of new physics with the Large Hadron Collider (LHC) [34–38].”
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Abratenko, P., Alterkait, O., Aldana, D. A., Arellano, L. G., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B. R., Barnard, A. C. L., Barr, G. D., et al. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.110.092010 · Cited by 4
Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling
Gambhir, R., Osathapan, A., & Thaler, J. (2024) · arXiv.org · doi:10.48550/arxiv.2403.08854 · Cited by 3
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Background
Neural networks for boosted di-τ identification
Tamir, N., Bessudo, I., Chen, B.-P., Raiko, H., & Barak, L. (2023) · Journal of Instrumentation · doi:10.1088/1748-0221/19/07/p07004 · Cited by 3
“Better tagging algorithms are critical in the success of current and future HEP experiments’ goals – both for improving sensitivities in offline analysis [20–27] and for designing more efficient triggers for online operation [28–33].”
Background
Probing light fermiophobic Higgs boson via diphoton jets at the HL-LHC
Wang, D., Cho, J.-H., Kim, J., Lee, S., Sanyal, P., & Song, J. (2023) · Physical Review D · doi:10.1103/physrevd.109.015017 · Cited by 3
Top Quarks from Tevatron to the LHC
Jung, A. W., & Kieseler, J. (2023) · Symmetry · doi:10.3390/sym15101915 · Cited by 5
Annihilation photon GAN source model for PET Monte Carlo simulation
Sarrut, D., Etxebeste, A., Kaprélian, T., Saporta, A., & Létang, J. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acdfb1 · Cited by 3
Machine-learning-based prediction of parameters of secondaries in hadronic showers using calorimetric observables
Chadeeva, M., & Korpachev, S. (2022) · Journal of Instrumentation · doi:10.1088/1748-0221/17/10/p10031 · Cited by 3
Machine learning of the well-known things
Долотин, В. В., Morozov, A. A., & Popolitov, A. V. (2023) · Theoretical and Mathematical Physics · doi:10.1134/s0040577923030091 · Cited by 2
Application of deep learning in top pair and single top quark production at the LHC
Ahmed, I., Zada, A., Waqas, M., & Ashraf, M. U. (2022) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-023-04409-z · Cited by 3
Influence of QCD parton showers in deep learning invisible Higgs bosons through vector boson fusion
Konar, P., & Ngairangbam, V. S. (2022) · Physical Review D · doi:10.1103/physrevd.105.113003 · Cited by 3
Top squark signal significance enhancement by different machine learning algorithms
Fraga, J. F., Ronald, R., Solano, J., Juan, M., & Ávila, C. (2022) · International Journal of Modern Physics A · doi:10.1142/s0217751x22501974 · Cited by 3
“The Large Hadron Collider (LHC) experiments have been applying different ML techniques to analyze and extract relevant information from the large amount of data collected by each specific detector [14].”
Method
ThickBrick: optimal event selection and categorization in high energy physics. Part I. Signal discovery
Matchev, K. T., & Shyamsundar, P. (2021) · Journal of High Energy Physics · doi:10.1007/jhep03(2021)291 · Cited by 7
Ontology, neural networks, and the social sciences
Strohmaier, D. (2020) · Synthese · doi:10.1007/s11229-020-03002-6 · Cited by 7
Higgs self-coupling measurements using deep learning in the $$ b\overline{b}b\overline{b} $$ final state
Amacker, J., Balunas, W., Beresford, L., Bortoletto, D., Frost, J., Issever, C., Liu, J., McKee, J., Micheli, A., Saenz, S. R. P., et al. (2020) · doi:10.1007/jhep12(2020)115 · Cited by 3
“Signal characterisation (λhhh vs λ j 6=i hhh) and background suppression (S vs B) are demanding classification problems seeing promising adoption of deep neural network (DNN) solutions in particle physics [77–87].”
BackgroundMethod
Information Condensing Active LearningPreprint
Jain, S., Liu, G., & Gifford, D. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07916 · Cited by 2
“They have been applied successfully in a variety of areas including biology (Ching et al., 2018), chemistry (Sanchez-Lengeling & AspuruGuzik, 2018), physics (Guest et al., 2018), and materials engineering (Aspuru-Guzik & Persson, 2018).”
Machine learning-based predictions of directionality and charge of cosmic muons—a simulation study using the mICAL detector
Samuel, D., Samalan, A., Kuttan, M. O., & Murgod, L. (2019) · Journal of Instrumentation · doi:10.1088/1748-0221/14/11/p11020 · Cited by 3
Using holistic event information in the triggerPreprint
Bourgeois, D., Stahl, S., & Fitzpatrick, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.00711 · Cited by 3
Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer
Xuan, T., Borca‐Tasciuc, G., Zhu, Y., Sun, Y. K., Dean, C. T., Shi, Z., & Yu, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26409-2_4 · Cited by 4
“It has proven its success in many tasks such as jet identification [9,20,23], particle identification [12,19,24], energy regression [12,15,36], and fast simulation [12,31,33].”
Method
Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders
de la Fuente, G. R., & Thaler, J. (2026) · arXiv.org · doi:10.48550/arxiv.2607.24921 · Cited by 2
“More recently, the rise of deep learning has led to the development of classifiers that leverage lower-level features to discriminate jet flavors [35–49].”
EasyScan_HEP 2: LLM-agent parameter-scan workflows in high energy physics
Xiao, Y., Yue, Y.-F., & Zhang, Y. (2026) · The European Physical Journal C · doi:10.1140/epjc/s10052-026-16358-9 · Cited by 2
Comprehensive mass predictions: from triply heavy baryons to pentaquarks
Rostami, S., Olamaei, A., Malekhosseini, M., & Azizi, K. (2026) · The European Physical Journal C · doi:10.1140/epjc/s10052-026-15881-z · Cited by 2
Learning the Standard Model Manifold: Bayesian Latent Diffusion for Collider Anomaly Detection
Patel, J., & Dorigo, T. (2026) · doi:10.48550/arxiv.2603.06754 · Cited by 2
“Supervised techniques rely on labeled signal and background samples to train classifiers, but their effectiveness is limited by the absence of reliable BSM signal labels and the risk of bias toward specific models [15].”
Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
Konar, P., Ngairangbam, V. S., Spannowsky, M., & Srivastava, D. (2025) · Journal of High Energy Physics · doi:10.1007/jhep03(2026)219 · Cited by 2
Shedding Light on Dark Matter at the LHC with Machine Learning
Arganda, E., Rios, M. D. L., Perez, A. D., Roy, S., Seoane, R. M. S., & Wagner, C. E. M. (2025) · Physical Review D · doi:10.1103/m1cd-1sfb · Cited by 2
Neural scaling laws for deep regression on domain image data of twisted magnets
Čadež, T., & Kim, K.-M. (2025) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/ae484c · Cited by 2
Searching for top-philic heavy resonances in boosted four-top final states
Darm'e, L., Fuks, B., Li, H.-L., Maltoni, M., & Touchèque, J. (2025) · Journal of High Energy Physics · doi:10.1007/jhep11(2025)091 · Cited by 2
“…the deployment of machine-learning-based techniques that exploit the full set of jet constituents and their properties has led to significant advances in jet tagging performance, these developments representing one of the most exciting innovations of the latest experimental runs at the LHC [1– 3].”
Deep Learning and Model Independence
King, M. (2025) · doi:10.48550/arxiv.2507.03438 · Cited by 2
“…words, “A comparison between the performance of deep networks (DNs) in signal–background classification and that of shallow networks (NNs) with a variety of low-and high-level features demonstrates that DNs with only low-level features outperform all other approaches,” (Guest et al., 2018, p. 169).”
Enhancing Sensitivity for Di-Higgs Boson Searches Using Anomaly Detection and Supervised Machine Learning Techniques
Chekanov, S. V., Islam, W., & Luongo, N. (2025) · doi:10.48550/arxiv.2504.12418 · Cited by 2
Applicability evaluation of selected xAI methods for machine learning algorithms for signal parameters extraction
Dimitrova, K., Kozhuharov, V., & Petkov, P. (2025) · Journal of Physics, Conference Series · doi:10.1088/1742-6596/3002/1/012005 · Cited by 2
“Deep learning techniques, in particular, have demonstrated significant potential in managing the intricate, high-dimensional data produced by the Large Hadron Collider [4].”
Development of the machine learning-based online trigger algorithms for heavy flavor events selection in sPHENIX experiment
Chen, Y., Chen, K., Lang, L., Zhang, B., Li, H., Cheng, Z.-P., Liu, J., Wang, Y.-P., Liu, M., & Shi, Z. (2025) · Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment · doi:10.1016/j.nima.2025.170435 · Cited by 2
Unfolding Particle Detector Acceptance in High Energy Physics with Generative AI
Alghamdi, T., Vittorini, T., Spreafico, M., Battaglieri, M., Sato, N., & Li, Y. (2024) · IEEE International Conference on Tools with Artificial Intelligence · doi:10.1109/ictai62512.2024.00138 · Cited by 2
“ML-based event generators (MLEGs) learn directly from data, enabling tasks like fast simulation, event generation, smearing corrections, and data reconstruction [11]–[14].”
Umami: A Python toolkit for jet flavour tagging
Barr, J., Birk, J., Draguet, M. A., Franchellucci, S., Froch, A., Gadow, P. P., Guest, D., Guth, M., Hartman, N. M., Kagan, M., et al. (2024) · The Journal of Open Source Software · doi:10.21105/joss.05833 · Cited by 4
Self-citation
Fast multilabel classification of HEP constraints with deep learning
Binjonaid, M. (2024) · Physical Review D · doi:10.1103/physrevd.111.075010 · Cited by 2
“These can be grouped into several areas, including signal-to-background analysis, jet-tagging, parameter space scanning, learning properties and constraints, and predicting likelihoods, among many other applications (for example [3–17]).”
Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques
Verdone, A., Devoto, A., Sebastiani, C., Carmignani, J., D'Onofrio, M., Giagu, S., Scardapane, S., & Panella, M. (2024) · arXiv.org · doi:10.48550/arxiv.2407.14859 · Cited by 2
“Edges of fully connected graph are not shown for clarity. in addressing the challenges posed by the LHC data [20].”
Background
Automated feature selection procedure for particle jet classification
Luca, A. D., Cristoforetti, M., Follega, F., Iuppa, R., & Mascione, D. (2023) · Nuclear Physics B · doi:10.1016/j.nuclphysb.2023.116182 · Cited by 2
Methodology for the Application of Deep Neural Networks in Searches for New Physics at Colliders and Statistical Interpretation of Expected Results
Abasov, E., Belobrova, M. I., Volkov, P., Vorotnikov, G., Dudko, L., Zaborenko, A., Perfilov, M., & Sivakova, E. S. (2022) · Physics of Atomic Nuclei · doi:10.1134/s1063778822060023 · Cited by 2
Reconstructing masses for semi-invisibly decaying particles pair-produced at lepton colliders
Yang, J.-M., Zhang, Y., Zhu, P.-X., & Zhu, R. (2022) · Physical Review D · doi:10.1103/physrevd.108.075015 · Cited by 2
Predicting the Masses of Exotic Hadrons with Data Augmentation Using Multilayer Perceptron
Bahtiyar, H. (2022) · International Journal of Modern Physics A · doi:10.1142/s0217751x23500033 · Cited by 2
“Neural networks are proven to make successful predictions in nuclear physics [9–14], and particle physics [15–22].”
Background
Exploring the universality of hadronic jet classification
Cheung, K., Chung, Y., Hsu, S.-C., & Nachman, B. (2022) · The European Physical Journal C · doi:10.1140/epjc/s10052-022-11084-4 · Cited by 2
Interpreting a Machine Learning Model for Detecting Gravitational Waves
Safarzadeh, M., Khan, A., Huerta, E. A., & Wattenberg, M. (2022) · arXiv.org · doi:10.48550/arxiv.2202.07399 · Cited by 2
“…to make precision-scale measurements and predictions that are revolutionizing multiple ﬁelds: cosmology, agriculture, personalized nutrition, genomics and particle physics [Guest et al., 2018, Huerta et al., 2019, Narita et al., 2020, Guo et al., 2021, Uddin et al., 2019, Chen et al., 2021].”
Background
Another Unorthodox Introduction to QCD and now Machine Learning
Larkoski, A. (2020) · doi:10.48550/arxiv.2008.09673 · Cited by 2
Unifying supervised learning and VAEs - automating statistical inference in high-energy physics
Glüsenkamp, T. (2020) · arXiv.org · doi:10.48550/arxiv.2008.05825 · Cited by 2
“A typical application of deep learning in experimental high-energy physics is concerned with the reconstruction of particle interactions [2].”
Background
The Impact of Constant Field of Attention on Properties of Contextual Neural Networks
Burnell, E., Wołk, K., Waliczek, K., & Kern, R. (2020) · Asian Conference on Intelligent Information and Database Systems · doi:10.1007/978-3-030-42058-1_31 · Cited by 2
Statistical Issues in Particle Physics
Lyons, L. (2020) · Particle Physics Reference Library · doi:10.1007/978-3-642-03606-4_15 · Cited by 2
“This involves the use of sophisticated techniques[45] for achieving nearly optimal extraction of information from data, but which are still relatively unfamiliar to many scientists.”
Method
Black boxes in black hole imaging
Doboszewski, J., & Elder, J. (2026) · Synthese · doi:10.1007/s11229-026-05751-2 · Cited by 1
“The question for our purposes here is thus the following: ought we (either in general, or at least in some particular cases, such as astrophysical contexts and ngEHT in particular) favor or even demand either explainable or interpretable ML?”
Self-Supervised Calibration of Scientific Instruments Using Physical Consistency Constraints
Rejmund, M., & Lemasson, A. (2026) · arXiv.org · doi:10.48550/arxiv.2606.29466 · Cited by 1
“In nuclear and particle physics, deep neural networks have been applied successfully to detector reconstruction pipelines, significantly improving processing speed and analysis reproducibility (Radovic et al. [3], Guest et al. [4], Carleo et al. [5], Albertsson et al. [6]).”
Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics
Schott, M., & Flek, L. (2026) · arXiv.org · doi:10.48550/arxiv.2605.07471 · Cited by 1
Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD
Kou, W., & Chen, X.-R. (2026) · Chinese Physics C, High Energy Physics & Nuclear Physics · doi:10.1088/1674-1137/aea284 · Cited by 1
Extraction of the color dipole amplitude with physics-informed neural networks
Kou, W., & Chen, X.-R. (2026) · Physics Letters B · doi:10.1016/j.physletb.2026.140507 · Cited by 1
Interpolation of unitaries with time-dependent Hamiltonians via deep learning
Guerra, A., Uzcátegui-Contreras, D., Delgado, A., & Gómez, E. S. (2026) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/aeb505 · Cited by 1
“Within physics, neural networks have been successfully applied to the analysis of high-dimensional collider data [7], gravitational wave detection [8], quantum state reconstruction from measurements [9, 10], the combination of marginals into global quantum states [11], and the solution of…”
Quantifying the effects of noise in a quantum convolutional neural network
Fan, Z.-Y., Lau, J., & Kwek, L. (2025) · SciPost Physics Core · doi:10.21468/scipostphyscore.8.4.093 · Cited by 1
“Today, machine learning (ML) has found many widespread applications such as gravitational wave astronomy [1–3], medical imaging [4, 5], data classification [6–8], and experimental particle data [9].”

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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