mlsanityData to 14 Oct 2026

Machine Learning for Anomaly Detection in Particle Physics

arXiv 2312.14190 ↗First posted 20 Dec 2023physics.data-an cs.LG hep-excited by 30

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Abstract (arXiv)

The detection of out-of-distribution data points is a common task in particle physics. It is used for monitoring complex particle detectors or for identifying rare and unexpected events that may be indicative of new phenomena or physics beyond the Standard Model. Recent advances in Machine Learning for anomaly detection have encouraged the utilization of such techniques on particle physics problems. This review article provides an overview of the state-of-the-art techniques for anomaly detection in particle physics using machine learning. We discuss the challenges associated with anomaly detection in large and complex data sets, such as those produced by high-energy particle colliders, and highlight some of the successful applications of anomaly detection in particle physics experiments.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 57 of its 182 references that are in the map. First posted on arXiv 20 Dec 2023.

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57 of 182 references.

References · 57

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 12; 2026: 11.

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  1. 2024Multiple testing for signal-agnostic searches of new physics with machine learningcited by 7
  2. 2025Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlationscited by 5
  3. 2025Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCcited by 5
  4. 2024Guided Quantum Compression for High Dimensional Data Classificationcited by 3
  5. 2024Universal New Physics Latent Spacecited by 3
  6. 2025Enhancing anomaly detection with topology-aware autoencoderscited by 3
  7. 2025Agents of Discoverycited by 3
  8. 2026Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practicecited by 3
  9. 2025Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extractioncited by 2
  10. 2025AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testingcited by 2
  11. 2025Learning Reduced Representations for Quantum Classifierscited by 2
  12. 2026Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliderscited by 2
18 more
  1. 2026Generative Models and Statistical Validationcited by 2
  2. 2024Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A surveycited by 1
  3. 2024Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHCcited by 1
  4. 2025A Step Toward Interpretability: Smearing the Likelihoodcited by 1
  5. 2025Sparse, self-organizing ensembles of local kernels detect rare statistical anomaliescited by 1
  6. 2026A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptationcited by 1
  7. 2026Can AI Follow In Einstein's Footsteps?cited by 1
  8. 2024Anomaly Detection from a Tensor Train Perspectivecited by 0
  9. 2024AI-based particle track identification in scintillating fibres read out with imaging sensorscited by 0
  10. 2025The Artificial Scientist -- in-transit Machine Learning of Plasma Simulationscited by 0
  11. 2025Generator Based Inference (GBI)cited by 0
  12. 2025Shedding Light on Dark Matter at the LHC with Machine Learningcited by 0
  13. 2026Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approachcited by 0
  14. 2026Learning Standard Model structure from LHC data with Riemannian flow matchingcited by 0
  15. 2026Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experimentscited by 0
  16. 2026Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliderscited by 0
  17. 2026Contrastive Learning for Interpretable Anomaly Detection at Collider Experimentscited by 0
  18. 2026Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHCcited by 0