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

{"title":"用直接扩散启动（-外壳","authors":"Anja Butter, Tomas Jezo, Michael Klasen, Mathias Kuschick, Sofia Palacios Schweitzer, Tilman Plehn","doi":"10.21468/scipostphyscore.7.3.064","DOIUrl":null,"url":null,"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.","PeriodicalId":21682,"journal":{"name":"SciPost Physics","volume":"53 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Kicking it off(-shell) with direct diffusion\",\"authors\":\"Anja Butter, Tomas Jezo, Michael Klasen, Mathias Kuschick, Sofia Palacios Schweitzer, Tilman Plehn\",\"doi\":\"10.21468/scipostphyscore.7.3.064\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":21682,\"journal\":{\"name\":\"SciPost Physics\",\"volume\":\"53 1\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SciPost Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.21468/scipostphyscore.7.3.064\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SciPost Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.21468/scipostphyscore.7.3.064","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}

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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

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.