Caio Daumann, Mauro Donega, Johannes Erdmann, Massimiliano Galli, Jan Lukas Späh, Davide Valsecchi
{"title":"One Flow to Correct Them all: Improving Simulations in High-Energy Physics with a Single Normalising Flow and a Switch.","authors":"Caio Daumann, Mauro Donega, Johannes Erdmann, Massimiliano Galli, Jan Lukas Späh, Davide Valsecchi","doi":"10.1007/s41781-024-00125-0","DOIUrl":null,"url":null,"abstract":"<p><p>Simulated events are key ingredients in almost all high-energy physics analyses. However, imperfections in the simulation can lead to sizeable differences between the observed data and simulated events. The effects of such mismodelling on relevant observables must be corrected either effectively via scale factors, with weights or by modifying the distributions of the observables and their correlations. We introduce a correction method that transforms one multidimensional distribution (simulation) into another one (data) using a simple architecture based on a single normalising flow with a boolean condition. We demonstrate the effectiveness of the method on a physics-inspired toy dataset with non-trivial mismodelling of several observables and their correlations.</p>","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":"8 1","pages":"15"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316724/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computing and Software for Big Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s41781-024-00125-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/10 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
Simulated events are key ingredients in almost all high-energy physics analyses. However, imperfections in the simulation can lead to sizeable differences between the observed data and simulated events. The effects of such mismodelling on relevant observables must be corrected either effectively via scale factors, with weights or by modifying the distributions of the observables and their correlations. We introduce a correction method that transforms one multidimensional distribution (simulation) into another one (data) using a simple architecture based on a single normalising flow with a boolean condition. We demonstrate the effectiveness of the method on a physics-inspired toy dataset with non-trivial mismodelling of several observables and their correlations.