mlsanityData to 14 Oct 2026

Variational Autoencoders for New Physics Mining at the Large Hadron Collider

arXiv 1811.10276 ↗First posted 26 Nov 2018hep-ex cs.LG hep-phcited by 41

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Abstract (arXiv)

Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn't make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC.

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Nov 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 12; 2022: 9; 2023: 11; 2024: 2; 2025: 4; 2026: 3.

41 papers.

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  1. 2021A Living Review of Machine Learning for Particle Physicscited by 78
  2. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  3. 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
  4. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 30
  5. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 29
  6. 2023Self-Supervised Learning with Lie Symmetries for Partial Differential Equationscited by 29
  7. 2021Better Latent Spaces for Better Autoencoderscited by 21
  8. 2022Learning new physics efficiently with nonparametric methodscited by 21
  9. 2023Quantum anomaly detection in the latent space of proton collision events at the LHCcited by 21
  10. 2021Topological Obstructions to Autoencodingcited by 20
  11. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  12. 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
29 more
  1. 2021Online-compatible Unsupervised Non-resonant Anomaly Detectioncited by 15
  2. 2022Lorentz group equivariant autoencoderscited by 15
  3. 2021Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detectioncited by 13
  4. 2023Anomalies, Representations, and Self-Supervisioncited by 11
  5. 2021Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distancecited by 10
  6. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  7. 2023CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimationcited by 10
  8. 2023Explainable Representation Learning of Small Quantum Statescited by 10
  9. 2023Improving new physics searches with diffusion models for event observables and jet constituentscited by 10
  10. 2021Autoencoders for Semivisible Jet Detectioncited by 9
  11. 2023Unravelling physics beyond the standard model with classical and quantum anomaly detectioncited by 9
  12. 2022Interpretable Uncertainty Quantification in AI for HEPcited by 8
  13. 2022Neural Embedding: Learning the Embedding of the Manifold of Physics Datacited by 8
  14. 2021Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoderscited by 7
  15. 2022Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Modelscited by 5
  16. 2025Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCcited by 5
  17. 2022Invariant Representation Driven Neural Classifier for Anti-QCD Jet Taggingcited by 4
  18. 2022Machine-Learning Compression for Particle Physics Discoveriescited by 3
  19. 2023Versatile Energy-Based Probabilistic Models for High Energy Physicscited by 2
  20. 2024Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Datacited by 2
  21. 2025AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testingcited by 2
  22. 2023A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data & Overparameterization?cited by 1
  23. 2022Microscopy is All You Needcited by 0
  24. 2024AI-based particle track identification in scintillating fibres read out with imaging sensorscited by 0
  25. 2025TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searchescited by 0
  26. 2025Strengthening Anomaly Awarenesscited by 0
  27. 2026Classical and Hybrid Quantum Machine Learning for Trigger-Like Event Selection on CMS Open Data: An Eight-Qubit, PCA-Constrained Benchmarkcited by 0
  28. 2026Likelihood-Based Unsupervised Anomaly Detection in CMS Dijet Eventscited by 0
  29. 2026Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHCcited by 0