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Machine Learning in the Search for New Fundamental Physics

arXiv 2112.03769 ↗First posted 7 Dec 2021hep-ph hep-ex physics.data-ancited by 50

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Abstract (arXiv)

Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments. While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present review.

Key facts. Cited by 50 papers in the map (data through 14 Oct 2026). Builds on 82 of its 312 references that are in the map. First posted on arXiv 7 Dec 2021.

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82 of 312 references.

References · 82

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  12. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 133
38 more
  1. 2020Lorentz Group Equivariant Neural Network for Particle Physicscited by 121
  2. 2015Approximating Likelihood Ratios with Calibrated Discriminative Classifierscited by 90
  3. 2018Energy Flow Networks: Deep Sets for Particle Jetscited by 84
  4. 2021A Living Review of Machine Learning for Particle Physicscited by 78
  5. 2020Anomaly Detection with Density Estimationcited by 72
  6. 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
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  10. 2017Classification without labels: Learning from mixed samples in high energy physicscited by 43
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  12. 2015Jet-Images -- Deep Learning Editioncited by 41
  13. 2018Variational Autoencoders for New Physics Mining at the Large Hadron Collidercited by 41
  14. 2019MadMiner: Machine learning-based inference for particle physicscited by 32
  15. 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
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  21. 2018Constraining Effective Field Theories with Machine Learningcited by 26
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  23. 2020Fast inference of Boosted Decision Trees in FPGAs for particle physicscited by 26
  24. 2018A Guide to Constraining Effective Field Theories with Machine Learningcited by 25
  25. 2018Generative Models for Fast Calorimeter Simulation.LHCb casecited by 25
  26. 2018Graph Neural Networks for IceCube Signal Classificationcited by 24
  27. 2017QCD-Aware Recursive Neural Networks for Jet Physicscited by 22
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  29. 2017Weakly Supervised Classification in High Energy Physicscited by 21
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  31. 2018Novelty Detection Meets Collider Physicscited by 20
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Citing papers per year: 2022: 10; 2023: 13; 2024: 11; 2025: 3; 2026: 13.

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  2. 2022On scientific understanding with artificial intelligencecited by 70
  3. 2023Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical lawscited by 55
  4. 2024OmniJet-α: The first cross-task foundation model for particle physicscited by 43
  5. 2023Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbertcited by 18
  6. 2023High-dimensional and Permutation Invariant Anomaly Detectioncited by 12
  7. 2023The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligencecited by 12
  8. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
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  10. 2023Scattering with Neural Operatorscited by 7
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38 more
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  10. 2022Feature Selection with Distance Correlationcited by 3
  11. 2023Non-parametric Hypothesis Tests for Distributional Group Symmetrycited by 3
  12. 2024Guided Quantum Compression for High Dimensional Data Classificationcited by 3
  13. 2024Universal New Physics Latent Spacecited by 3
  14. 2025Agents of Discoverycited by 3
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  16. 2026JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physicscited by 3
  17. 2023AdamMCMC: Combining Metropolis Adjusted Langevin with Momentum-based Optimizationcited by 2
  18. 2026Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-trainingcited by 2
  19. 2026CDRL: Certification-Driven Reinforcement Learning for Neutrino Flavor Model Discoverycited by 2
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  21. 2024Chaining thoughts and LLMs to learn DNA structural biophysicscited by 1
  22. 2026Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systemscited by 1
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  26. 2023IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonicscited by 0
  27. 2023Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectorscited by 0
  28. 2023Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimationcited by 0
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  30. 2023A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networkscited by 0
  31. 2024Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Poolingcited by 0
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  33. 2025Learning Degenerate Manifolds of Frustrated Magnets with Boltzmann Machinescited by 0
  34. 2026Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physicscited by 0
  35. 2026Learning Standard Model structure from LHC data with Riemannian flow matchingcited by 0
  36. 2026Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliderscited by 0
  37. 2026Likelihood-Based Unsupervised Anomaly Detection in CMS Dijet Eventscited by 0
  38. 2026Can Deep Learning Achieve Cross-Physics Mapping?cited by 0