mlsanityData to 7 Oct 2026

Anomaly Detection with Density Estimation

arXiv 2001.04990 ↗First posted 14 Jan 2020hep-ph hep-ex physics.data-ancited by 71

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Abstract (arXiv)

We leverage recent breakthroughs in neural density estimation to propose a new unsupervised anomaly detection technique (ANODE). By estimating the probability density of the data in a signal region and in sidebands, and interpolating the latter into the signal region, a likelihood ratio of data vs. background can be constructed. This likelihood ratio is broadly sensitive to overdensities in the data that could be due to localized anomalies. In addition, a unique potential benefit of the ANODE method is that the background can be directly estimated using the learned densities. Finally, ANODE is robust against systematic differences between signal region and sidebands, giving it broader applicability than other methods. We demonstrate the power of this new approach using the LHC Olympics 2020 R&D Dataset. We show how ANODE can enhance the significance of a dijet bump hunt by up to a factor of 7 with a 10% accuracy on the background prediction. While the LHC is used as the recurring example, the methods developed here have a much broader applicability to anomaly detection in physics and beyond.

Key facts. Cited by 71 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 16; 2022: 11; 2023: 21; 2024: 8; 2025: 8; 2026: 7.

71 papers.

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  1. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  2. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  3. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
  4. 2021Fair Normalizing Flowscited by 38
  5. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
  6. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 28
  7. 2023Distribution learning via neural differential equations: a nonparametric statistical perspectivecited by 22
  8. 2021Better Latent Spaces for Better Autoencoderscited by 20
  9. 2022Learning new physics efficiently with nonparametric methodscited by 20
  10. 2021Bump Hunting in Latent Spacecited by 19
  11. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  12. 2021Understanding Event-Generation Networks via Uncertaintiescited by 17
38 more
  1. 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
  2. 2021E Pluribus Unum Ex Machina: Learning from Many Collider Events at Oncecited by 15
  3. 2021Online-compatible Unsupervised Non-resonant Anomaly Detectioncited by 15
  4. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 15
  5. 2021Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detectioncited by 13
  6. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  7. 2023High-dimensional and Permutation Invariant Anomaly Detectioncited by 12
  8. 2022nu-Flows: Conditional Neutrino Regressioncited by 11
  9. 2023Learning Likelihood Ratios with Neural Network Classifierscited by 11
  10. 2021Latent Space Refinement for Deep Generative Modelscited by 10
  11. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  12. 2023Anomalies, Representations, and Self-Supervisioncited by 10
  13. 2023CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimationcited by 10
  14. 2023Improving new physics searches with diffusion models for event observables and jet constituentscited by 10
  15. 2023Unravelling physics beyond the standard model with classical and quantum anomaly detectioncited by 9
  16. 2022IRC-safe Graph Autoencoder for unsupervised anomaly detectioncited by 8
  17. 2023Anomaly Detection with Variance Stabilized Density Estimationcited by 8
  18. 2022"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detectioncited by 7
  19. 2022Neural Embedding: Learning the Embedding of the Manifold of Physics Datacited by 7
  20. 2023Calorimeter shower superresolutioncited by 7
  21. 2021New Methods and Datasets for Group Anomaly Detection From Fundamental Physicscited by 6
  22. 2022Invertible Monotone Operators for Normalizing Flowscited by 6
  23. 2023Fast Particle-based Anomaly Detection Algorithm with Variational Autoencodercited by 6
  24. 2024Unifying Simulation and Inference with Normalizing Flowscited by 6
  25. 2024Multi-objective Deep Learning: Taxonomy and Survey of the State of the Artcited by 6
  26. 2021Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contaminationcited by 5
  27. 2023Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Testscited by 5
  28. 2022Invariant Representation Driven Neural Classifier for Anti-QCD Jet Taggingcited by 4
  29. 2022Resonant Anomaly Detection with Multiple Reference Datasetscited by 4
  30. 2023An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contaminationcited by 4
  31. 2023Generative Invertible Quantum Neural Networkscited by 3
  32. 2024BUFF: Boosted Decision Tree based Ultra-Fast Flow matchingcited by 3
  33. 2026Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practicecited by 3
  34. 2021GBHT: Gradient Boosting Histogram Transform for Density Estimationcited by 2
  35. 2022New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimationcited by 2
  36. 2023Improving the performance of weak supervision searches using transfer and meta-learningcited by 2
  37. 2023TERM Model: Tensor Ring Mixture Model for Density Estimationcited by 2
  38. 2025Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detectioncited by 2

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