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Autore principale: Kuschick, Mathias
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2412.17783
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author Kuschick, Mathias
author_facet Kuschick, Mathias
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2412_17783
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Encoding off-shell effects in top pair production in Direct Diffusion networks
Kuschick, Mathias
High Energy Physics - Phenomenology
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.
title Encoding off-shell effects in top pair production in Direct Diffusion networks
topic High Energy Physics - Phenomenology
url https://arxiv.org/abs/2412.17783