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arXiv · 2311.17175

Kicking it Off(-shell) with Direct Diffusion

Anja Butter·Tomas Jezo·Michael Klasen·Mathias Kuschick·Sofia Palacios Schweitzer·Tilman Plehn
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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.

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Anja Butter, Tomas Jezo, Michael Klasen, Mathias Kuschick, Sofia Palacios Schweitzer, Tilman Plehn. 2023-11-28. Kicking it Off(-shell) with Direct Diffusion. https://arxiv.org/abs/2311.17175

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