Jet Image Generation in High Energy Physics Using Diffusion Models

--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 Fr echet 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. IFFUSION models have been used for a wide range of image generation tasks, including grayscale images, RGB color images, hyperspectral images, and physics-based images. Grayscale and color image generation using diffusion models have demonstrated significant advancements in capturing details and color distributions. In grayscale image generation, these models effectively reproduce variations in intensity and texture, as shown in recent studies [1], [2].