{"title":"用生成对抗网络模拟LHCb强子量热计","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":"{\"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}","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}
Simulating the LHCb hadron calorimeter with generative adversarial networks
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.