mlsanityData to 14 Oct 2026

Multimodal Generative Flows for LHC Jets

arXiv 2509.01736 ↗First posted 1 Sep 2025hep-ph cs.LGcited by 1

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Abstract (arXiv)

Generative modeling of high-energy collisions at the Large Hadron Collider (LHC) offers a data-driven route to simulations, anomaly detection, among other applications. A central challenge lies in the hybrid nature of particle-cloud data: each particle carries continuous kinematic features and discrete quantum numbers such as charge and flavor. We introduce a transformer-based multimodal flow that extends flow-matching with a continuous-time Markov jump bridge to jointly model LHC jets with both modalities. Trained on CMS Open Data, our model can generate high fidelity jets with realistic kinematics, jet substructure and flavor composition.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 33 references that are in the map. First posted on arXiv 1 Sep 2025.

Builds on

20 of 33 references.

References · 20

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  3. 2022Flow Matching for Generative Modelingcited by 5,702
  4. 2017On Calibration of Modern Neural Networkscited by 4,960
  5. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  6. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  7. 2022Building Normalizing Flows with Stochastic Interpolantscited by 995
  8. 2022A Continuous Time Framework for Discrete Denoising Modelscited by 451
  9. 2024Discrete Flow Matchingcited by 317
  10. 2024Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Designcited by 305
  11. 2024Flow Matching Guide and Codecited by 301
  12. 2022Particle Transformer for Jet Taggingcited by 98
8 more
  1. 2024Generator Matching: Generative modeling with arbitrary Markov processescited by 86
  2. 2024How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Frameworkcited by 48
  3. 2023EPiC-GAN: Equivariant Point Cloud Generation for Particle Jetscited by 23
  4. 2023EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusioncited by 17
  5. 2024Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physicscited by 14
  6. 2024Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion modelscited by 8
  7. 2023Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Informationcited by 7
  8. 2025TFG-Flow: Training-free Guidance in Multimodal Generative Flowcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Generative models on phase spacecited by 0

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  4. 2026Learning Standard Model structure from LHC data with Riemannian flow matching
  5. 2023End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
  6. 2024Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN
  7. 2023Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation
  8. 2024Quantum Diffusion Model for Quark and Gluon Jet Generation