如何对GAN事件进行加权

Mathias Backes, A. Butter, T. Plehn, R. Winterhalder
{"title":"如何对GAN事件进行加权","authors":"Mathias Backes, A. Butter, T. Plehn, R. Winterhalder","doi":"10.21468/SCIPOSTPHYS.10.4.089","DOIUrl":null,"url":null,"abstract":"Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain in simulation speed.","PeriodicalId":8457,"journal":{"name":"arXiv: High Energy Physics - Phenomenology","volume":"18 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":"{\"title\":\"How to GAN Event Unweighting\",\"authors\":\"Mathias Backes, A. Butter, T. Plehn, R. Winterhalder\",\"doi\":\"10.21468/SCIPOSTPHYS.10.4.089\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain in simulation speed.\",\"PeriodicalId\":8457,\"journal\":{\"name\":\"arXiv: High Energy Physics - Phenomenology\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"29\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv: High Energy Physics - Phenomenology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21468/SCIPOSTPHYS.10.4.089\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv: High Energy Physics - Phenomenology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21468/SCIPOSTPHYS.10.4.089","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 29

摘要

近年来,神经网络在事件生成方面取得了重大进展。最大的问题仍然是这些新方法如何将LHC模拟加速到即将到来的LHC运行所需的水平。我们的目标是标准模拟的已知瓶颈,并展示了如何通过生成网络改进其不加权过程。这可能会导致模拟速度的显著提高。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
How to GAN Event Unweighting
Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain in simulation speed.
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
自引率
0.00%
发文量
0
期刊最新文献
The Logarithmic Contributions to the Polarized and Operator Matrix Elements in Deeply Inelastic Scattering Fast flavor instabilities and the search for neutrino angular crossings Chiral susceptibility in a dense thermomagnetic QCD medium within the hard thermal loop approximation On the radiation of an arbitrary moving permanent magnetic moment Higgs alignment and the top quark
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1