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Exploring Optimal Transport for Event-Level Anomaly Detection at the Large Hadron Collider

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arxiv 2401.15542 v2 pith:RGJGXBN3 submitted 2024-01-28 hep-ph

Nathaniel Craig , Jessica N. Howard , Hancheng Li This is my paper · ORCID
classification hep-ph
keywords anomalydetectionmethodscolliderevent-levelhadronlargelearning
verification ladder T0 review T1 audit T2 compute T3 formal

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Anomaly detection is a promising, model-agnostic strategy to find physics beyond the Standard Model. State-of-the-art machine learning methods offer impressive performance on anomaly detection tasks, but interpretability, resource, and memory concerns motivate considering a wide range of alternatives. We explore using the 2-Wasserstein distance from optimal transport theory, both as an anomaly score and as input to interpretable machine learning methods, for event-level anomaly detection at the Large Hadron Collider. The choice of ground space plays a key role in optimizing performance. We comment on the feasibility of implementing these methods in the L1 trigger system.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing anomaly detection with topology-aware autoencoders

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    Autoencoders with latent spaces shaped like S^2, S^2×S^2, or RP^2, matched to the phase-space topology of the background, reduce spurious reconstruction errors and give a small but consistent anomaly-detection gain ov...

  2. Multi-scale Optimal Transport for Complete Collider Events

    hep-ph 2025-01 conditional novelty 7.0 of 10

    A hierarchical optimal transport distance, built by measuring events as distributions of jets whose shapes are measured by optimal transport, improves classification of simulated LHC events.

  3. Machine learning fully hadronic events with spectral functions

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    Spectral functions from two-point correlations serve as multiplicity-independent ML inputs and improve expected gluino mass reach by 150-250 GeV in a fully hadronic ttbar vs gluino benchmark.

  4. Factorization for Collider Dataspace Correlators

    hep-ph 2025-04 conditional novelty 6.0 of 10

    Derives a factorization theorem and NLL resummation for SEMD-based event correlators, predicting a universal ledge in quark-jet non-Gaussianity.

  5. A Step Toward Interpretability: Smearing the Likelihood

    hep-ph 2025-01 conditional novelty 6.0 of 10

    Smearing the likelihood over an energy metric reveals the physical scales used by a jet classifier, and the needed smearing radius follows a power-law scaling with dataset size.

  6. Optimal Transport Event Representation for Anomaly Detection

    hep-ph 2025-12 conditional novelty 5.0 of 10

    Adding a few optimal-transport-based features to standard jet observables nearly doubles anomaly-detection significance at 0.5% signal injection on LHC Olympics benchmarks.

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