Speaker
Mathias Kuschick
(Universität Münster)
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. The idea behind this sampling from on-shell events is that the generative network does not have to reproduce the on-shell features and can focus on the additional and relatively smooth off-shell extension. It generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features.
Primary authors
Anja Butter
(ITP Heidelberg)
Mathias Kuschick
(Universität Münster)
Michael Klasen
(WWU Münster)
Sofia Palacios Schweitzer
(Universität Heidelberg)
Tilman Plehn
(Heidelberg University)
Tomáš Ježo
(Universität Münster)
