mlsanityData to 14 Oct 2026

Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information

arXiv 2312.00123 ↗First posted 30 Nov 2023hep-ph cs.LG hep-excited by 7

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditioned on the jet type, so that a single model can be used to generate the ten different jet types of JetClass. For the first time, we also introduce a generative model that goes beyond the kinematic features of jet constituents. The JetClass dataset includes more features, such as particle-ID and track impact parameter, and we demonstrate that our CNF can accurately model all of these additional features as well. Our generative model for JetClass expands on the versatility of existing jet generation techniques, enhancing their potential utility in high-energy physics research, and offering a more comprehensive understanding of the generated jets.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 27 of its 61 references that are in the map. First posted on arXiv 30 Nov 2023.

Builds on

27 of 61 references.

Closest ancestors · 6

Same direction, newest first

  1. 2023EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion29 Sep
  2. 2023PC-Droid: Faster diffusion and improved quality for particle cloud generation13 Jul
  3. 2023Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation10 Jul
  4. 2023Improving and generalizing flow-based generative models with minibatch optimal transport1 Feb
  5. 2022Flow Matching for Generative Modeling6 Oct
  6. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow7 Sep

Other references · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  6. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  7. 2018Neural Ordinary Differential Equationscited by 3,779
  8. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,043
  9. 2022Building Normalizing Flows with Stochastic Interpolantscited by 995
  10. 2022Particle Transformer for Jet Taggingcited by 98
  11. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
  12. 2021Particle Cloud Generation with Message Passing Generative Adversarial Networkscited by 46
9 more
  1. 2023CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 33
  2. 2022Evaluating generative models in high energy physicscited by 31
  3. 2023PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physicscited by 27
  4. 2019LHC analysis-specific datasets with Generative Adversarial Networkscited by 26
  5. 2023EPiC-GAN: Equivariant Point Cloud Generation for Particle Jetscited by 23
  6. 2021Generative Networks for Precision Enthusiastscited by 21
  7. 2020Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)cited by 20
  8. 2023CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 20
  9. 2023Attention to Mean-Fields for Particle Cloud Generationcited by 12

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 4; 2025: 2.

7 papers.

  1. 2024OmniJet-α: The first cross-task foundation model for particle physicscited by 43
  2. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  3. 2023Improving new physics searches with diffusion models for event observables and jet constituentscited by 10
  4. 2024Generative Unfolding with Distribution Mappingcited by 6
  5. 2025Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networkscited by 3
  6. 2024SIGMA: Single Interpolated Generative Model for Anomaliescited by 2
  7. 2025Multimodal Generative Flows for LHC Jetscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives
  2. 2026Generative Modeling with Orbit-Space Particle Flow Matching
  3. 2026A Kinetic Energy Perspective of Flow Matching
  4. 2023Equivariant flow matching
  5. 2025Generative Modeling with Continuous Flows: Sample Complexity of Flow Matching
  6. 2026VTV-FM: Flow Matching through Variational Terminal-Velocity Closure
  7. 2025Jet Image Generation in High Energy Physics Using Diffusion Models
  8. 2026Transition Flow Matching