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 t...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Butter, Anja, Jezo, Tomas, Klasen, Michael, Kuschick, Mathias, Sofia Palacios Schweitzer, Plehn, Tilman
Format: Article
Language:English
Subjects:
Kinematics
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422