mlsanityData to 7 Oct 2026

A Living Review of Machine Learning for Particle Physics

arXiv 2102.02770 ↗First posted 2 Feb 2021hep-ph cs.LG hep-excited by 77

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Abstract (arXiv)

Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. As a living document, it will be updated as often as possible to incorporate the latest developments. A list of proper (unchanging) reviews can be found within. Papers are grouped into a small set of topics to be as useful as possible. Suggestions and contributions are most welcome, and we provide instructions for participating.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). Builds on 101 of its 374 references that are in the map. First posted on arXiv 2 Feb 2021.

Builds on

101 of 374 references.

References · 101

  1. 2015Variational Inference with Normalizing Flowscited by 2,161
  2. 2019The frontier of simulation-based inferencecited by 490
  3. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 147
  4. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 132
  5. 2020Flows for simultaneous manifold learning and density estimationcited by 117
  6. 2020Equivariant flow-based sampling for lattice gauge theorycited by 102
  7. 2015Approximating Likelihood Ratios with Calibrated Discriminative Classifierscited by 87
  8. 2018Energy Flow Networks: Deep Sets for Particle Jetscited by 83
  9. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
  10. 2020Anomaly Detection with Density Estimationcited by 71
  11. 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
  12. 2018Mining gold from implicit models to improve likelihood-free inferencecited by 60
38 more
  1. 2016Learning to Pivot with Adversarial Networkscited by 55
  2. 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
  3. 2017Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesiscited by 46
  4. 2020Anomaly Detection With Conditional Variational Autoencoderscited by 46
  5. 2020Lattice gauge equivariant convolutional neural networkscited by 45
  6. 2019Learning representations of irregular particle-detector geometry with distance-weighted graph networkscited by 43
  7. 2017Classification without labels: Learning from mixed samples in high energy physicscited by 42
  8. 2019Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physicscited by 42
  9. 2020Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4MLcited by 42
  10. 2015Jet-Images -- Deep Learning Editioncited by 41
  11. 2018Variational Autoencoders for New Physics Mining at the Large Hadron Collidercited by 40
  12. 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networkscited by 36
  13. 2020Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physicscited by 34
  14. 2016Parameterized Machine Learning for High-Energy Physicscited by 33
  15. 2020Variational Autoencoders for Anomalous Jet Taggingcited by 33
  16. 2019MadMiner: Machine learning-based inference for particle physicscited by 32
  17. 2019OmniFold: A Method to Simultaneously Unfold All Observablescited by 31
  18. 2020Simulation Assisted Likelihood-free Anomaly Detectioncited by 27
  19. 2016A Convolutional Neural Network Neutrino Event Classifiercited by 26
  20. 2019LHC analysis-specific datasets with Generative Adversarial Networkscited by 26
  21. 2020Fast inference of Boosted Decision Trees in FPGAs for particle physicscited by 26
  22. 2018Generative Models for Fast Calorimeter Simulation.LHCb casecited by 25
  23. 2017Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeterscited by 23
  24. 2020Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAscited by 23
  25. 2020DCTRGAN: Improving the Precision of Generative Models with Reweightingcited by 22
  26. 2020Jet tagging in the Lund plane with graph networkscited by 22
  27. 2016Deep learning in color: towards automated quark/gluon jet discriminationcited by 21
  28. 2017Weakly Supervised Classification in High Energy Physicscited by 21
  29. 2020Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inferencecited by 21
  30. 2019Neural Networks for Full Phase-space Reweighting and Parameter Tuningcited by 20
  31. 2018JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physicscited by 18
  32. 2020End-to-end Sinkhorn Autoencoder with Noise Generatorcited by 17
  33. 2018INFERNO: Inference-Aware Neural Optimisationcited by 16
  34. 2020Linearized Optimal Transport for Collider Eventscited by 16
  35. 2020Jet Flavour Classification Using DeepJetcited by 16
  36. 2019Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learningcited by 15
  37. 2019AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH casecited by 15
  38. 2021E Pluribus Unum Ex Machina: Learning from Many Collider Events at Oncecited by 15

50 most cited of 101 All 101 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 12; 2022: 16; 2023: 11; 2024: 10; 2025: 13; 2026: 15.

77 papers.

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  1. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  2. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
  3. 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
  4. 2023Explainable Equivariant Neural Networks for Particle Physics: PELICANcited by 22
  5. 2021Topological Obstructions to Autoencodingcited by 20
  6. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  7. 2021Online-compatible Unsupervised Non-resonant Anomaly Detectioncited by 15
  8. 2022Lorentz group equivariant autoencoderscited by 15
  9. 2022Applications of Machine Learning to Lattice Quantum Field Theorycited by 14
  10. 2025Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Modelscited by 14
  11. 2021Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detectioncited by 13
  12. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
38 more
  1. 2022SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learningcited by 12
  2. 2023Attention to Mean-Fields for Particle Cloud Generationcited by 12
  3. 2023High-dimensional and Permutation Invariant Anomaly Detectioncited by 12
  4. 2021A Cautionary Tale of Decorrelating Theory Uncertaintiescited by 11
  5. 2021Optimising simulations for diphoton production at hadron colliders using amplitude neural networkscited by 9
  6. 2022A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacousticcited by 7
  7. 2022Null Hypothesis Test for Anomaly Detectioncited by 7
  8. 2024Quantum Vision Transformers for Quark-Gluon Classificationcited by 7
  9. 2025Physics-Driven Learning for Inverse Problems in Quantum Chromodynamicscited by 7
  10. 2025RINO: Renormalization Group Invariance with No Labelscited by 6
  11. 2022Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physicscited by 5
  12. 2023Scaling Laws in Jet Classificationcited by 5
  13. 2025Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlationscited by 5
  14. 2022Resonant Anomaly Detection with Multiple Reference Datasetscited by 4
  15. 2023mathbbZ2× mathbbZ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networkscited by 4
  16. 2024KAN we improve on HEP classification tasks? Kolmogorov-Arnold Networks applied to an LHC physics examplecited by 4
  17. 2022Bias and Priors in Machine Learning Calibrations for High Energy Physicscited by 3
  18. 2022Data Science and Machine Learning in Educationcited by 3
  19. 2022Exploration of Parameter Spaces Assisted by Machine Learningcited by 3
  20. 2022Machine-Learned Exclusion Limits without Binningcited by 3
  21. 2023Configurable calorimeter simulation for AI applicationscited by 3
  22. 2023Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicistscited by 3
  23. 2023Artificial Intelligence for the Electron Ion Collider (AI4EIC)cited by 3
  24. 2024Jet Discrimination with Quantum Complete Graph Neural Networkcited by 3
  25. 2025A Perspective on Symbolic Machine Learning in Physical Sciencescited by 3
  26. 2026Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuningcited by 3
  27. 2026Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactionscited by 3
  28. 2022Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approachcited by 2
  29. 2022New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimationcited by 2
  30. 2023The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flowscited by 2
  31. 2024Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural networkcited by 2
  32. 2024FAIR Universe HiggsML Uncertainty Dataset and Competitioncited by 2
  33. 2025Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuningcited by 2
  34. 2025Agents of Discoverycited by 2
  35. 2026Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physicscited by 2
  36. 2021Towards a method to anticipate dark matter signals with deep learning at the LHCcited by 1
  37. 2021Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learningcited by 1
  38. 2022Learning Feynman Diagrams using Graph Neural Networkscited by 1

50 most cited of 77 All 77 (JSON)