用直接扩散启动（-外壳

IF 4.6 2区物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY

SciPost Physics Pub Date : 2024-09-16 DOI:10.21468/scipostphyscore.7.3.064

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

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引用次数: 0

摘要

大型强子对撞机背景中的壳外效应对精确预测至关重要，同时也是模拟的挑战。我们提出了一种基于扩散神经网络转换高维分布的新方法，并用它从简单得多的壳上运动学过程生成壳外运动学过程。我们将其应用于一个在 LO 产生顶对的玩具例子，展示了我们的方法如何快速而精确地生成壳外构型，同时再现甚至具有挑战性的壳内特征。

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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.

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来源期刊

SciPost Physics Physics and Astronomy-Physics and Astronomy (all)

CiteScore

8.20

自引率

12.70%

发文量

315

审稿时长

10 weeks

期刊介绍： SciPost Physics publishes breakthrough research articles in the whole field of Physics, covering Experimental, Theoretical and Computational approaches. Specialties covered by this Journal: - Atomic, Molecular and Optical Physics - Experiment - Atomic, Molecular and Optical Physics - Theory - Biophysics - Condensed Matter Physics - Experiment - Condensed Matter Physics - Theory - Condensed Matter Physics - Computational - Fluid Dynamics - Gravitation, Cosmology and Astroparticle Physics - High-Energy Physics - Experiment - High-Energy Physics - Theory - High-Energy Physics - Phenomenology - Mathematical Physics - Nuclear Physics - Experiment - Nuclear Physics - Theory - Quantum Physics - Statistical and Soft Matter Physics.