Speaker
Sofia Palacios Schweitzer
(ITP Heidelberg)
Description
Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We show how a generative diffusion network learns off-shell kinematics given the much simpler on-shell process. It generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features.
Primary authors
Dr
Anja Butter
(LPNHE, Paris)
Mathias Kuschick
(Munster U., ITP)
Prof.
Michael Klasen
(Munster U., ITP)
Sofia Palacios Schweitzer
(ITP Heidelberg)
Tilman Plehn
(Universität Heidelberg, ITP)
Dr
Tomas Jezo
(Munster U., ITP)
