Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2412.17783 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918321311449088 |
|---|---|
| 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 |