Kicking it Off(-shell) with Direct Diffusion
High Energy Physics - Phenomenology
2024-08-27 v3
Abstract
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.
Cite
@article{arxiv.2311.17175,
title = {Kicking it Off(-shell) with Direct Diffusion},
author = {Anja Butter and Tomas Jezo and Michael Klasen and Mathias Kuschick and Sofia Palacios Schweitzer and Tilman Plehn},
journal= {arXiv preprint arXiv:2311.17175},
year = {2024}
}