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Linearized Optimal Transport for Collider Events

arXiv 2008.08604 ↗First posted 19 Aug 2020hep-ph hep-ex stat.MLcited by 16

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Abstract (arXiv)

We introduce an efficient framework for computing the distance between collider events using the tools of Linearized Optimal Transport (LOT). This preserves many of the advantages of the recently-introduced Energy Mover's Distance, which quantifies the "work" required to rearrange one event into another, while significantly reducing the computational cost. It also furnishes a Euclidean embedding amenable to simple machine learning algorithms and visualization techniques, which we demonstrate in a variety of jet tagging examples. The LOT approximation lowers the threshold for diverse applications of the theory of optimal transport to collider physics.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 0; 2023: 4; 2024: 0; 2025: 4; 2026: 1.

16 papers.

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  1. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  2. 2021Topological Obstructions to Autoencodingcited by 20
  3. 2023Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein spacecited by 20
  4. 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
  5. 2021Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distancecited by 10
  6. 2021The Signed Cumulative Distribution Transform for 1-D Signal Analysis and Classificationcited by 9
  7. 2021An Exploration of Learnt Representations of W Jetscited by 8
  8. 2023Linear Optimal Partial Transport Embeddingcited by 8
  9. 2021Transport away your problems: Calibrating stochastic simulations with optimal transportcited by 5
  10. 2023Scaling Laws in Jet Classificationcited by 5
  11. 2023Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHCcited by 1
  12. 2025A Step Toward Interpretability: Smearing the Likelihoodcited by 1
4 more
  1. 2025Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectivescited by 1
  2. 2025Non-Gaussianities in Collider Metric Binningcited by 0
  3. 2025Linearized Optimal Transport for Analysis of High-Dimensional Point-Cloud and Single-Cell Datacited by 0
  4. 2026Learning the Geometry of Collider Events with Metric-Aware Deep Setscited by 0