TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysProc.18.005
TI  - Encoding off-shell effects in top pair production in direct diffusion networks
PY  - 2026/01/29
UR  - https://scipost.org/SciPostPhysProc.18.005
JF  - SciPost Physics Proceedings
JA  - SciPost Phys. Proc.
VL  - 
IS  - 18
SP  - 005
A1  - Kuschick, Mathias
AB  - To meet the precision targets of upcoming LHC runs in the simulation of top pair production events it is essential to also consider off-shell effects. Due to their great computational cost I propose to encode them in neural networks. For that I use a combination of neural networks that take events with approximate off-shell effects and transform them into events that match those obtained with full off-shell calculations. This was shown to work reliably and efficiently at leading order. Here I discuss first steps extending this method to include higher order effects.
ER  -

@Article{10.21468/SciPostPhysProc.18.005,
	title={{Encoding off-shell effects in top pair production in direct diffusion networks}},
	author={Mathias Kuschick},
	journal={SciPost Phys. Proc.},
	pages={005},
	year={2026},
	publisher={SciPost},
	doi={10.21468/SciPostPhysProc.18.005},
	url={https://scipost.org/10.21468/SciPostPhysProc.18.005},
}