TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.8.1.026
TI  - Jet diffusion versus JetGPT – Modern networks for the LHC
PY  - 2025/03/03
UR  - https://www.scipost.org/SciPostPhysCore.8.1.026
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 8
IS  - 1
SP  - 026
A1  - Butter, Anja
AU  - Huetsch, Nathan
AU  - Palacios Schweitzer, Sofia
AU  - Plehn, Tilman
AU  - Sorrenson, Peter
AU  - Spinner, Jonas
AB  - We introduce two diffusion models and an autoregressive transformer for LHC physics simulations. Bayesian versions allow us to control the networks and capture training uncertainties. After illustrating their different density estimation methods for simple toy models, we discuss their advantages for Z plus jets event generation. While diffusion networks excel through their precision, the transformer scales best with the phase space dimensionality. Given the different training and evaluation speed, we expect LHC physics to benefit from dedicated use cases for normalizing flows, diffusion models, and autoregressive transformers.
ER  -

@Article{10.21468/SciPostPhysCore.8.1.026,
	title={{Jet diffusion versus JetGPT – Modern networks for the LHC}},
	author={Anja Butter and Nathan Huetsch and Sofia Palacios Schweitzer and Tilman Plehn and Peter Sorrenson and Jonas Spinner},
	journal={SciPost Phys. Core},
	volume={8},
	pages={026},
	year={2025},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.8.1.026},
	url={https://scipost.org/10.21468/SciPostPhysCore.8.1.026},
}