Jet Image Generation in High Energy Physics Using Diffusion Models

2025-07-31

AI Summary

This work pioneers the application of diffusion models to jet image generation from LHC proton-proton collision events, directly modeling the spatial distribution of particle kinematic variables in image space. We propose two class-conditional generative approaches—score-based diffusion models and consistency models—and systematically evaluate them on the JetNet dataset, which includes quark, gluon, W/Z, and top-quark jets. Compared to latent-variable paradigms, our image-space generation framework achieves both higher fidelity and improved computational efficiency. Notably, consistency models substantially outperform score-based diffusion models, yielding significant improvements in quantitative metrics such as the Fréchet Inception Distance (FID) and producing higher-quality jet images. The enhanced realism and efficiency of the generated images make this approach well-suited for high-energy physics simulation and analysis tasks, offering a promising new direction for data-driven jet modeling.

Technology Category

Computer Vision: Diffusion Models for VisionMachine Learning: Large Multimodal Models (LMMs)Natural Language Processing: Generation

Application Category

Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphsUser Modeling, Personalization and Recommendation: Federated recommendation systems and personalizationWeb Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Generate jet images from proton-proton collisions using diffusion models
• Compare performance of score-based and consistency models for image fidelity
• Improve computational efficiency and accuracy in High Energy Physics research

Innovation

• Uses diffusion models for jet image generation
• Maps kinematic variables to 2D images
• Compares score-based and consistency models