Kicking it off(-shell) with direct diffusion
Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We present a novel method to transform high-dimensional distributions based on a diffusion neural network and use it to generate a process with off-shell kinematics from the much simpler on-shell one. Applied to a toy example of top pair production at LO we show how our method generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features.
Authors
- Michael Klasen (ORCID: https://orcid.org/0000-0002-4665-3088)
- Tilman Plehn (ORCID: https://orcid.org/0000-0001-5660-7790)
- Tomáš Ježo (ORCID: https://orcid.org/0000-0002-1334-7607)
- Anja Butter (ORCID: https://orcid.org/0000-0003-4525-6256)
- Mathias Kuschick
- Sofia Palacios Schweitzer
Institutions
- Université Paris Cité (FR)
- Heidelberg University (DE)
- University of Münster (DE)
- Sorbonne Paris Cité (FR)
Publication Details
- Journal
- SciPost Physics Core
- Published
- 2024-09-16
- DOI
- https://doi.org/10.21468/scipostphyscore.7.3.064
- Citations
- 18
- Primary Topic
- Particle physics theoretical and experimental studies
- Type
- article
- Field-Weighted Citation Impact
- 11.87
Funders
- Baden-Württemberg Stiftung
- Strong
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- Centre National de la Recherche Scientifique
- Westfälische Wilhelms-Universität Münster
- Sorbonne Université
- Université de Paris
- Institut National de Physique Nucléaire et de Physique des Particules