mlsanityData to 7 Oct 2026

Machine Learning in Nuclear Physics

arXiv 2112.02309 ↗First posted 4 Dec 2021nucl-th cs.LG hep-excited by 4

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Abstract (arXiv)

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 51 of its 435 references that are in the map. First posted on arXiv 4 Dec 2021.

Builds on

51 of 435 references.

References · 51

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2017A Survey on Multi-Task Learningcited by 838
  4. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 147
  5. 2018A high-bias, low-variance introduction to Machine Learning for physicistscited by 108
  6. 2020Equivariant flow-based sampling for lattice gauge theorycited by 102
  7. 2020Sampling using SU(N) gauge equivariant flowscited by 83
  8. 2017Generalizing Hamiltonian Monte Carlo with Neural Networkscited by 52
  9. 2020Lattice gauge equivariant convolutional neural networkscited by 45
  10. 2016Learning Aligned Cross-Modal Representations from Weakly Aligned Datacited by 34
  11. 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
  12. 2020Gauge equivariant neural networks for quantum lattice gauge theoriescited by 27
38 more
  1. 2021Flow-based sampling for fermionic lattice field theoriescited by 27
  2. 2021Flow-based sampling for multimodal and extended-mode distributions in lattice field theorycited by 25
  3. 2016Deep learning in color: towards automated quark/gluon jet discriminationcited by 21
  4. 2019Neural Canonical Transformation with Symplectic Flowscited by 16
  5. 2020Jet Flavour Classification Using DeepJetcited by 16
  6. 2019AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH casecited by 15
  7. 2020Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratorycited by 15
  8. 2020Towards Novel Insights in Lattice Field Theory with Explainable Machine Learningcited by 12
  9. 2019DeepRICH: Learning Deeply Cherenkov Detectorscited by 9
  10. 2019Cherenkov Detectors Fast Simulation Using Neural Networkscited by 8
  11. 2018Machine Learning Methods for Track Classification in the AT-TPCcited by 7
  12. 2019Complex-valued neural networks for machine learning on non-stationary physical datacited by 7
  13. 2020Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FELcited by 7
  14. 2021A Novel Approach for Classification and Forecasting of Time Series in Particle Acceleratorscited by 7
  15. 2021Deep Learning Hamiltonian Monte Carlocited by 7
  16. 2018Bayesian approach to model-based extrapolation of nuclear observablescited by 6
  17. 2019Bayesian optimization in ab initio nuclear physicscited by 6
  18. 2019Deep learning based pulse shape discrimination for germanium detectorscited by 6
  19. 2019Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networkscited by 6
  20. 2021Machine learning spectral functions in lattice QCDcited by 6
  21. 2020Quantified limits of the nuclear landscapecited by 5
  22. 2020Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnosticscited by 5
  23. 2021Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Boostercited by 5
  24. 2021Uncertainty aware anomaly detection to predict errant beam pulses in the SNS acceleratorcited by 5
  25. 2014Galaxy Formation: Bayesian History Matching for the Observable Universecited by 4
  26. 2019Neutron drip line in the Ca region from Bayesian model averagingcited by 4
  27. 2020Statistical aspects of nuclear mass modelscited by 3
  28. 2020Online Multi-Objective Particle Accelerator Optimization of the AWAKE Electron Beam Line for Simultaneous Emittance and Orbit Controlcited by 3
  29. 2020Topological defects and confinement with machine learning: the case of monopoles in compact electrodynamicscited by 3
  30. 2021Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentationcited by 3
  31. 2018Automatic trajectory recognition in Active Target Time Projection Chambers data by means of hierarchical clusteringcited by 2
  32. 2018Deep learning: Extrapolation tool for ab initio nuclear theorycited by 2
  33. 2019Beyond the proton drip line: Bayesian analysis of proton-emitting nucleicited by 2
  34. 2020Efficient Machine Learning Approach for Optimizing the Timing Resolution of a High Purity Germanium Detectorcited by 2
  35. 2020Unsupervised Learning for Identifying Events in Active Target Experimentscited by 2
  36. 2020A Neural Network Perturbation Theory Based on the Born Seriescited by 2
  37. 2020Trees and Forests in Nuclear Physicscited by 1
  38. 2021Does Bayesian Model Averaging improve polynomial extrapolations? Two toy problems as testscited by 1

50 most cited of 51 All 51 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 2.

4 papers.

  1. 2024Quantum Vision Transformers for Quark-Gluon Classificationcited by 7
  2. 2023Local Bayesian Dirichlet mixing of imperfect modelscited by 2
  3. 2026Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systemscited by 1
  4. 2026Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discoverycited by 0