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.
@article{arxiv.2412.17783,
title = {Encoding off-shell effects in top pair production in Direct Diffusion networks},
author = {Mathias Kuschick},
journal= {arXiv preprint arXiv:2412.17783},
year = {2026}
}
Comments
Talk at the 17th International Workshop on Top Quark Physics (Top2024), 22-27 September 2024