mlsanityData to 14 Oct 2026

Online-compatible Unsupervised Non-resonant Anomaly Detection

arXiv 2111.06417 ↗First posted 11 Nov 2021cs.LG hep-ex hep-phcited by 15

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Abstract (arXiv)

There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on signal sensitivity. However, it is not enough to select anomalous events - there must also be a strategy to provide context to the selected events. We propose the first complete strategy for unsupervised detection of non-resonant anomalies that includes both signal sensitivity and a data-driven method for background estimation. Our technique is built out of two simultaneously-trained autoencoders that are forced to be decorrelated from each other. This method can be deployed offline for non-resonant anomaly detection and is also the first complete online-compatible anomaly detection strategy. We show that our method achieves excellent performance on a variety of signals prepared for the ADC2021 data challenge.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 31 of its 114 references that are in the map. First posted on arXiv 11 Nov 2021.

Builds on

31 of 114 references.

References · 31

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  2. 2013Auto-Encoding Variational Bayescited by 4,053
  3. 2019An Introduction to Variational Autoencoderscited by 1,000
  4. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 133
  5. 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 80
  6. 2021A Living Review of Machine Learning for Particle Physicscited by 78
  7. 2020Anomaly Detection with Density Estimationcited by 72
  8. 2016Learning to Pivot with Adversarial Networkscited by 55
  9. 2020Anomaly Detection With Conditional Variational Autoencoderscited by 46
  10. 2021Fast convolutional neural networks on FPGAs with hls4mlcited by 42
  11. 2018Variational Autoencoders for New Physics Mining at the Large Hadron Collidercited by 41
  12. 2020Variational Autoencoders for Anomalous Jet Taggingcited by 33
19 more
  1. 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
  2. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 29
  3. 2020Simulation Assisted Likelihood-free Anomaly Detectioncited by 27
  4. 2020Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAscited by 23
  5. 2021Better Latent Spaces for Better Autoencoderscited by 21
  6. 2013Partial Distance Correlation with Methods for Dissimilaritiescited by 20
  7. 2021Topological Obstructions to Autoencodingcited by 20
  8. 2021Bump Hunting in Latent Spacecited by 19
  9. 2020Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quarkcited by 18
  10. 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
  11. 2020Simulation-Assisted Decorrelation for Resonant Anomaly Detectioncited by 13
  12. 2021Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detectioncited by 13
  13. 2017Decorrelated Jet Substructure Tagging using Adversarial Neural Networkscited by 12
  14. 2020Unsupervised in-distribution anomaly detection of new physics through conditional density estimationcited by 12
  15. 2021A Cautionary Tale of Decorrelating Theory Uncertaintiescited by 11
  16. 2020Anomaly Awarenesscited by 10
  17. 2020Adversarial domain adaptation to reduce sample bias of a high energy physics classifiercited by 9
  18. 2021Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physicscited by 9
  19. 2021An Exploration of Learnt Representations of W Jetscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 4; 2024: 0; 2025: 2; 2026: 3.

15 papers.

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  1. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  2. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 30
  3. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  4. 2023Anomalies, Representations, and Self-Supervisioncited by 11
  5. 2023Improving new physics searches with diffusion models for event observables and jet constituentscited by 10
  6. 2022Neural Embedding: Learning the Embedding of the Manifold of Physics Datacited by 8
  7. 2022Machine-Learning Compression for Particle Physics Discoveriescited by 3
  8. 2022Machine-Learned Exclusion Limits without Binningcited by 3
  9. 2022Feature Selection with Distance Correlationcited by 3
  10. 2022Physics Community Needs, Tools, and Resources for Machine Learningcited by 2
  11. 2026FoMo X: Modular Explainability Signals for Outlier Detection Foundation Modelscited by 2
  12. 2026Unsupervised Symbolic Anomaly Detectioncited by 1
3 more
  1. 2025TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searchescited by 0
  2. 2025Rare anomalies require large datasets: About proving the existence of anomaliescited by 0
  3. 2026RangeAD: Fast On-Model Anomaly Detectioncited by 0