{"title":"Simulating the LHCb hadron calorimeter with generative adversarial networks","authors":"D. Lancierini, P. Owen, N. Serra","doi":"10.5167/UZH-178913","DOIUrl":null,"url":null,"abstract":"Generative adversarial networks are known as a tool for fast simulation of data. Our aim is to research and develop a physical application of these tools by simulating LHCb hadron calorimeter (HCAL) in order to speed up the Monte Carlo datasets production.","PeriodicalId":13304,"journal":{"name":"Il Nuovo Cimento D","volume":"50 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Il Nuovo Cimento D","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5167/UZH-178913","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Generative adversarial networks are known as a tool for fast simulation of data. Our aim is to research and develop a physical application of these tools by simulating LHCb hadron calorimeter (HCAL) in order to speed up the Monte Carlo datasets production.
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