Jet Image Generation in High Energy Physics Using Diffusion Models
This provides incremental improvements in computational efficiency and accuracy for High Energy Physics research, specifically for simulating jet images in particle physics.
This paper tackles the problem of generating jet images from proton-proton collision events at the LHC by applying diffusion models for the first time in this context, achieving higher fidelity and stability with consistency models compared to score-based diffusion models as measured by metrics like FID.
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.