Jet Image Generation in High Energy Physics Using Diffusion Models

2025/08/01 by Ricardo Martí­nez, Martinez, Victor D., Vidya Manian +3
Medicine · Physics and Astronomy · #Artificial Intelligence (cs.AI) #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #High-Energy Particle Collisions Research #Machine Learning (cs.LG) #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.2508.00250

openalex publication_date 2025/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This article presents, for the first time, the application of diffusion models for generating jet images corresponding to proton-proton collision events at the Large Hadron Collider (LHC). The kinematic variables of quark, gluon, W-boson, Z-boson, and top quark jets from the JetNet simulation dataset are mapped to two-dimensional image representations. Diffusion models are trained on these images to learn the spatial distribution of jet constituents. We compare the performance of score-based diffusion models and consistency models in accurately generating class-conditional jet images. Unlike approaches based on latent distributions, our method operates directly in image space. The fidelity of the generated images is evaluated using several metrics, including the Fréchet Inception Distance (FID), which demonstrates that consistency models achieve higher fidelity and generation stability compared to score-based diffusion models. These advancements offer significant improvements in computational efficiency and generation accuracy, providing valuable tools for High Energy Physics (HEP) research.

Citations

• Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation
• CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Score-based Diffusion Models for Generating Liquid Argon Time Projection Chamber Images
• Consistency Models
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• Elucidating the Design Space of Diffusion-Based Generative Models
• Particle Cloud Generation with Message Passing Generative Adversarial Networks
• Diffusion Models Beat GANs on Image Synthesis
• Improved Denoising Diffusion Probabilistic Models
• Maximum Likelihood Training of Score-Based Diffusion Models
• Score-Based Generative Modeling through Stochastic Differential Equations
• Variational Autoencoders for Jet Simulation
• Denoising Diffusion Probabilistic Models
• Improved Techniques for Training Score-Based Generative Models
• Generative Modeling by Estimating Gradients of the Data Distribution
• Wasserstein GAN
• PointNet: Deep Learning on Point Sets for 3D Classification and\n Segmentation
• Deep Metric Learning via Lifted Structured Feature Embedding
• Deep Unsupervised Learning using Nonequilibrium Thermodynamics
• A Connection Between Score Matching and Denoising Autoencoders

Related

• Point cloud-based diffusion models for the Electron-Ion Collider
• Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation
• Improving new physics searches with diffusion models for event observables and jet constituents
• Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A survey
• Image-Based Jet Analysis