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}
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.
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