Jet Image Generation in High Energy Physics Using Diffusion Models
By: Victor D. Martinez, Vidya Manian, Sudhir Malik
Potential Business Impact:
Creates realistic pictures of tiny particle collisions.
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.
Similar Papers
Multimodal Generative Flows for LHC Jets
High Energy Physics - Phenomenology
Makes computer simulations of particle collisions better.
Towards Overcoming Data Scarcity in Nuclear Energy: A Study on Critical Heat Flux with Physics-consistent Conditional Diffusion Model
Machine Learning (CS)
Creates fake nuclear power data for better predictions.
Jet Image Tagging Using Deep Learning: An Ensemble Model
Data Analysis, Statistics and Probability
Helps scientists sort tiny particle pieces better.