mlsanityData to 7 Oct 2026

Lorentz group equivariant autoencoders

arXiv 2212.07347 ↗First posted 14 Dec 2022hep-ex cs.LGcited by 15

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Abstract (arXiv)

There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries. Such biases have been shown to make models more performant and interpretable, and reduce the amount of training data needed. To that end, we develop the Lorentz group autoencoder (LGAE), an autoencoder model equivariant with respect to the proper, orthochronous Lorentz group SO^+(3,1), with a latent space living in the representations of the group. We present our architecture and several experimental results on jets at the LHC and find it outperforms graph and convolutional neural network baseline models on several compression, reconstruction, and anomaly detection metrics. We also demonstrate the advantage of such an equivariant model in analyzing the latent space of the autoencoder, which can improve the explainability of potential anomalies discovered by such ML models.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 46 of its 101 references that are in the map. First posted on arXiv 14 Dec 2022.

Builds on

46 of 101 references.

References · 46

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34 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 1; 2025: 4; 2026: 4.

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  1. 2023Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principlescited by 22
  2. 2023Anomalies, Representations, and Self-Supervisioncited by 10
  3. 2023Fast Particle-based Anomaly Detection Algorithm with Variational Autoencodercited by 6
  4. 2023Oracle-Preserving Latent Flowscited by 5
  5. 2024Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoodscited by 5
  6. 2023mathbbZ2× mathbbZ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networkscited by 4
  7. 2025Enhancing anomaly detection with topology-aware autoencoderscited by 3
  8. 2025Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoderscited by 3
  9. 2023Lorentz Equivariant Model for Knowledge-Enhanced Hyperbolic Collaborative Filteringcited by 1
  10. 2025Learning symmetries in datasetscited by 1
  11. 2026jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillationcited by 1
  12. 2026Artificial Intelligence and Symmetries: Learning, Encoding, and Discovering Structure in Physical Datacited by 1
3 more
  1. 2025Learnable cut flow for high energy physicscited by 0
  2. 2026Generative models on phase spacecited by 0
  3. 2026Contrastive Learning for Interpretable Anomaly Detection at Collider Experimentscited by 0