mlsanityData to 14 Oct 2026

High-dimensional and Permutation Invariant Anomaly Detection

arXiv 2306.03933 ↗First posted 6 Jun 2023hep-ph cs.AI cs.LGcited by 12

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Abstract (arXiv)

Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particularly at the constituent level, incorporating desirable properties such as permutation invariance and variable-length inputs becomes difficult within popular density estimation methods. In this work, we introduce a permutation-invariant density estimator for particle physics data based on diffusion models, specifically designed to handle variable-length inputs. We demonstrate the efficacy of our methodology by utilizing the learned density as a permutation-invariant anomaly detection score, effectively identifying jets with low likelihood under the background-only hypothesis. To validate our density estimation method, we investigate the ratio of learned densities and compare to those obtained by a supervised classification algorithm.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 30 of its 104 references that are in the map. First posted on arXiv 6 Jun 2023.

Builds on

30 of 104 references.

References · 30

  1. 2017Attention Is All You Needcited by 60,361
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  12. 2020Anomaly Detection with Density Estimationcited by 72
18 more
  1. 2022Score-based Generative Models for Calorimeter Shower Simulationcited by 52
  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. 2017Classification without labels: Learning from mixed samples in high energy physicscited by 43
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  6. 2023CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 33
  7. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 29
  8. 2023PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physicscited by 27
  9. 2020DCTRGAN: Improving the Precision of Generative Models with Reweightingcited by 22
  10. 2023Inductive Simulation of Calorimeter Showers with Normalizing Flowscited by 22
  11. 2018Novelty Detection Meets Collider Physicscited by 20
  12. 2018JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physicscited by 18
  13. 2021Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detectioncited by 13
  14. 2023Anomalies, Representations, and Self-Supervisioncited by 11
  15. 2021Latent Space Refinement for Deep Generative Modelscited by 10
  16. 2022IRC-safe Graph Autoencoder for unsupervised anomaly detectioncited by 8
  17. 2023ELSA -- Enhanced latent spaces for improved collider simulationscited by 5
  18. 2023Weakly-Supervised Anomaly Detection in the Milky Waycited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 3; 2026: 2.

12 papers.

  1. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 30
  2. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  3. 2023Improving new physics searches with diffusion models for event observables and jet constituentscited by 10
  4. 2023PC-Droid: Faster diffusion and improved quality for particle cloud generationcited by 8
  5. 2024Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusioncited by 8
  6. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  7. 2024Unifying Simulation and Inference with Normalizing Flowscited by 6
  8. 2026Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practicecited by 3
  9. 2025Data-Driven High-Dimensional Statistical Inference with Generative Modelscited by 2
  10. 2024Variational inference for pile-up removal at hadron colliders with diffusion modelscited by 1
  11. 2025TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searchescited by 0
  12. 2026Contrastive Learning for Interpretable Anomaly Detection at Collider Experimentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
  2. 2025Research on Anomaly Detection Methods Based on Diffusion Models
  3. 2023TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models
  4. 2023ρ-Diffusion: A diffusion-based density estimation framework for computational physics
  5. 2023On Diffusion Modeling for Anomaly Detection
  6. 2023Time Series Anomaly Detection using Diffusion-based Models
  7. 2025A Survey on Diffusion Models for Anomaly Detection
  8. 2021Unsupervised learning of anomalous diffusion data