mlsanityData to 7 Oct 2026

Machine Learning in High Energy Physics Community White Paper

arXiv 1807.02876 ↗First posted 8 Jul 2018physics.comp-ph cs.LG hep-excited by 48

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Abstract (arXiv)

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2018.

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Citing papers per year: 2021: 13; 2022: 7; 2023: 8; 2024: 8; 2025: 8; 2026: 4.

48 papers.

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  1. 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
  2. 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
  3. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
  4. 2021A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHCcited by 20
  5. 2021E Pluribus Unum Ex Machina: Learning from Many Collider Events at Oncecited by 15
  6. 2025Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Modelscited by 14
  7. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  8. 2022SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learningcited by 12
  9. 2021A Cautionary Tale of Decorrelating Theory Uncertaintiescited by 11
  10. 2023Unravelling physics beyond the standard model with classical and quantum anomaly detectioncited by 9
  11. 2024Quantum Vision Transformers for Quark-Gluon Classificationcited by 7
  12. 2022End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networkscited by 6
36 more
  1. 2025Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlationscited by 5
  2. 2021Development of a Vertex Finding Algorithm using Recurrent Neural Networkcited by 4
  3. 2024Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfercited by 4
  4. 2022Data Science and Machine Learning in Educationcited by 3
  5. 2022Learning Tree Structures from Leaves For Particle Decay Reconstructioncited by 3
  6. 2022Machine-Learned Exclusion Limits without Binningcited by 3
  7. 2024PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstructioncited by 3
  8. 2024Decoding complexity: how machine learning is redefining scientific discoverycited by 3
  9. 2021Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Arraycited by 2
  10. 2021Graph Generative Models for Fast Detector Simulations in High Energy Physicscited by 2
  11. 2023Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surfacecited by 2
  12. 2025Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extractioncited by 2
  13. 2021Towards a method to anticipate dark matter signals with deep learning at the LHCcited by 1
  14. 2021Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learningcited by 1
  15. 2022Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic fieldcited by 1
  16. 2023Hyperparameter Optimization as a Service on INFN Cloudcited by 1
  17. 2023Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimetercited by 1
  18. 2024Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHCcited by 1
  19. 2025A Step Toward Interpretability: Smearing the Likelihoodcited by 1
  20. 2025Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platformcited by 1
  21. 2025KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Taggingcited by 1
  22. 2026A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptationcited by 1
  23. 2021Deep Learning strategies for ProtoDUNE raw data denoisingcited by 0
  24. 2021Learning to increase matching efficiency in identifying additional b-jets in the tbartbbarb processcited by 0
  25. 2021Domain-informed neural networks for interaction localization within astroparticle experimentscited by 0
  26. 2023Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectorscited by 0
  27. 2023Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectorscited by 0
  28. 2023What Machine Learning Can Do for Focusing Aerogel Detectorscited by 0
  29. 2024Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Poolingcited by 0
  30. 2024Automated and Holistic Co-design of Neural Networks and ASICs for Enabling In-Pixel Intelligencecited by 0
  31. 2024Calibrating for the Future:Enhancing Calorimeter Longevity with Deep Learningcited by 0
  32. 2025Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIFcited by 0
  33. 2025Shedding Light on Dark Matter at the LHC with Machine Learningcited by 0
  34. 2026Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physicscited by 0
  35. 2026ML-based Fast Simulation of FARICH Responsescited by 0
  36. 2026Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliderscited by 0